research about blended learning approach

• Research article
• Open access
• Published: 06 February 2017

Blended learning effectiveness: the relationship between student characteristics, design features and outcomes

• Mugenyi Justice Kintu   ORCID: orcid.org/0000-0002-4500-1168 1 , 2 ,
• Chang Zhu 2 &
• Edmond Kagambe 1  

International Journal of Educational Technology in Higher Education volume  14 , Article number:  7 ( 2017 ) Cite this article

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This paper investigates the effectiveness of a blended learning environment through analyzing the relationship between student characteristics/background, design features and learning outcomes. It is aimed at determining the significant predictors of blended learning effectiveness taking student characteristics/background and design features as independent variables and learning outcomes as dependent variables. A survey was administered to 238 respondents to gather data on student characteristics/background, design features and learning outcomes. The final semester evaluation results were used as a measure for performance as an outcome. We applied the online self regulatory learning questionnaire for data on learner self regulation, the intrinsic motivation inventory for data on intrinsic motivation and other self-developed instruments for measuring the other constructs. Multiple regression analysis results showed that blended learning design features (technology quality, online tools and face-to-face support) and student characteristics (attitudes and self-regulation) predicted student satisfaction as an outcome. The results indicate that some of the student characteristics/backgrounds and design features are significant predictors for student learning outcomes in blended learning.

Introduction

The teaching and learning environment is embracing a number of innovations and some of these involve the use of technology through blended learning. This innovative pedagogical approach has been embraced rapidly though it goes through a process. The introduction of blended learning (combination of face-to-face and online teaching and learning) initiatives is part of these innovations but its uptake, especially in the developing world faces challenges for it to be an effective innovation in teaching and learning. Blended learning effectiveness has quite a number of underlying factors that pose challenges. One big challenge is about how users can successfully use the technology and ensuring participants’ commitment given the individual learner characteristics and encounters with technology (Hofmann, 2014 ). Hofmann adds that users getting into difficulties with technology may result into abandoning the learning and eventual failure of technological applications. In a report by Oxford Group ( 2013 ), some learners (16%) had negative attitudes to blended learning while 26% were concerned that learners would not complete study in blended learning. Learners are important partners in any learning process and therefore, their backgrounds and characteristics affect their ability to effectively carry on with learning and being in blended learning, the design tools to be used may impinge on the effectiveness in their learning.

This study tackles blended learning effectiveness which has been investigated in previous studies considering grades, course completion, retention and graduation rates but no studies regarding effectiveness in view of learner characteristics/background, design features and outcomes have been done in the Ugandan university context. No studies have also been done on how the characteristics of learners and design features are predictors of outcomes in the context of a planning evaluation research (Guskey, 2000 ) to establish the effectiveness of blended learning. Guskey ( 2000 ) noted that planning evaluation fits in well since it occurs before the implementation of any innovation as well as allowing planners to determine the needs, considering participant characteristics, analyzing contextual matters and gathering baseline information. This study is done in the context of a plan to undertake innovative pedagogy involving use of a learning management system (moodle) for the first time in teaching and learning in a Ugandan university. The learner characteristics/backgrounds being investigated for blended learning effectiveness include self-regulation, computer competence, workload management, social and family support, attitude to blended learning, gender and age. We investigate the blended learning design features of learner interactions, face-to-face support, learning management system tools and technology quality while the outcomes considered include satisfaction, performance, intrinsic motivation and knowledge construction. Establishing the significant predictors of outcomes in blended learning will help to inform planners of such learning environments in order to put in place necessary groundwork preparations for designing blended learning as an innovative pedagogical approach.

Kenney and Newcombe ( 2011 ) did their comparison to establish effectiveness in view of grades and found that blended learning had higher average score than the non-blended learning environment. Garrison and Kanuka ( 2004 ) examined the transformative potential of blended learning and reported an increase in course completion rates, improved retention and increased student satisfaction. Comparisons between blended learning environments have been done to establish the disparity between academic achievement, grade dispersions and gender performance differences and no significant differences were found between the groups (Demirkol & Kazu, 2014 ).

However, blended learning effectiveness may be dependent on many other factors and among them student characteristics, design features and learning outcomes. Research shows that the failure of learners to continue their online education in some cases has been due to family support or increased workload leading to learner dropout (Park & Choi, 2009 ) as well as little time for study. Additionally, it is dependent on learner interactions with instructors since failure to continue with online learning is attributed to this. In Greer, Hudson & Paugh’s study as cited in Park and Choi ( 2009 ), family and peer support for learners is important for success in online and face-to-face learning. Support is needed for learners from all areas in web-based courses and this may be from family, friends, co-workers as well as peers in class. Greer, Hudson and Paugh further noted that peer encouragement assisted new learners in computer use and applications. The authors also show that learners need time budgeting, appropriate technology tools and support from friends and family in web-based courses. Peer support is required by learners who have no or little knowledge of technology, especially computers, to help them overcome fears. Park and Choi, ( 2009 ) showed that organizational support significantly predicts learners’ stay and success in online courses because employers at times are willing to reduce learners’ workload during study as well as supervisors showing that they are interested in job-related learning for employees to advance and improve their skills.

The study by Kintu and Zhu ( 2016 ) investigated the possibility of blended learning in a Ugandan University and examined whether student characteristics (such as self-regulation, attitudes towards blended learning, computer competence) and student background (such as family support, social support and management of workload) were significant factors in learner outcomes (such as motivation, satisfaction, knowledge construction and performance). The characteristics and background factors were studied along with blended learning design features such as technology quality, learner interactions, and Moodle with its tools and resources. The findings from that study indicated that learner attitudes towards blended learning were significant factors to learner satisfaction and motivation while workload management was a significant factor to learner satisfaction and knowledge construction. Among the blended learning design features, only learner interaction was a significant factor to learner satisfaction and knowledge construction.

The focus of the present study is on examining the effectiveness of blended learning taking into consideration learner characteristics/background, blended learning design elements and learning outcomes and how the former are significant predictors of blended learning effectiveness.

Studies like that of Morris and Lim ( 2009 ) have investigated learner and instructional factors influencing learning outcomes in blended learning. They however do not deal with such variables in the contexts of blended learning design as an aspect of innovative pedagogy involving the use of technology in education. Apart from the learner variables such as gender, age, experience, study time as tackled before, this study considers social and background aspects of the learners such as family and social support, self-regulation, attitudes towards blended learning and management of workload to find out their relationship to blended learning effectiveness. Identifying the various types of learner variables with regard to their relationship to blended learning effectiveness is important in this study as we embark on innovative pedagogy with technology in teaching and learning.

Literature review

This review presents research about blended learning effectiveness from the perspective of learner characteristics/background, design features and learning outcomes. It also gives the factors that are considered to be significant for blended learning effectiveness. The selected elements are as a result of the researcher’s experiences at a Ugandan university where student learning faces challenges with regard to learner characteristics and blended learning features in adopting the use of technology in teaching and learning. We have made use of Loukis, Georgiou, and Pazalo ( 2007 ) value flow model for evaluating an e-learning and blended learning service specifically considering the effectiveness evaluation layer. This evaluates the extent of an e-learning system usage and the educational effectiveness. In addition, studies by Leidner, Jarvenpaa, Dillon and Gunawardena as cited in Selim ( 2007 ) have noted three main factors that affect e-learning and blended learning effectiveness as instructor characteristics, technology and student characteristics. Heinich, Molenda, Russell, and Smaldino ( 2001 ) showed the need for examining learner characteristics for effective instructional technology use and showed that user characteristics do impact on behavioral intention to use technology. Research has dealt with learner characteristics that contribute to learner performance outcomes. They have dealt with emotional intelligence, resilience, personality type and success in an online learning context (Berenson, Boyles, & Weaver, 2008 ). Dealing with the characteristics identified in this study will give another dimension, especially for blended learning in learning environment designs and add to specific debate on learning using technology. Lin and Vassar, ( 2009 ) indicated that learner success is dependent on ability to cope with technical difficulty as well as technical skills in computer operations and internet navigation. This justifies our approach in dealing with the design features of blended learning in this study.

Learner characteristics/background and blended learning effectiveness

Studies indicate that student characteristics such as gender play significant roles in academic achievement (Oxford Group, 2013 ), but no study examines performance of male and female as an important factor in blended learning effectiveness. It has again been noted that the success of e- and blended learning is highly dependent on experience in internet and computer applications (Picciano & Seaman, 2007 ). Rigorous discovery of such competences can finally lead to a confirmation of high possibilities of establishing blended learning. Research agrees that the success of e-learning and blended learning can largely depend on students as well as teachers gaining confidence and capability to participate in blended learning (Hadad, 2007 ). Shraim and Khlaif ( 2010 ) note in their research that 75% of students and 72% of teachers were lacking in skills to utilize ICT based learning components due to insufficient skills and experience in computer and internet applications and this may lead to failure in e-learning and blended learning. It is therefore pertinent that since the use of blended learning applies high usage of computers, computer competence is necessary (Abubakar & Adetimirin, 2015 ) to avoid failure in applying technology in education for learning effectiveness. Rovai, ( 2003 ) noted that learners’ computer literacy and time management are crucial in distance learning contexts and concluded that such factors are meaningful in online classes. This is supported by Selim ( 2007 ) that learners need to posses time management skills and computer skills necessary for effectiveness in e- learning and blended learning. Self-regulatory skills of time management lead to better performance and learners’ ability to structure the physical learning environment leads to efficiency in e-learning and blended learning environments. Learners need to seek helpful assistance from peers and teachers through chats, email and face-to-face meetings for effectiveness (Lynch & Dembo, 2004 ). Factors such as learners’ hours of employment and family responsibilities are known to impede learners’ process of learning, blended learning inclusive (Cohen, Stage, Hammack, & Marcus, 2012 ). It was also noted that a common factor in failure and learner drop-out is the time conflict which is compounded by issues of family , employment status as well as management support (Packham, Jones, Miller, & Thomas, 2004 ). A study by Thompson ( 2004 ) shows that work, family, insufficient time and study load made learners withdraw from online courses.

Learner attitudes to blended learning can result in its effectiveness and these shape behavioral intentions which usually lead to persistence in a learning environment, blended inclusive. Selim, ( 2007 ) noted that the learners’ attitude towards e-learning and blended learning are success factors for these learning environments. Learner performance by age and gender in e-learning and blended learning has been found to indicate no significant differences between male and female learners and different age groups (i.e. young, middle-aged and old above 45 years) (Coldwell, Craig, Paterson, & Mustard, 2008 ). This implies that the potential for blended learning to be effective exists and is unhampered by gender or age differences.

Blended learning design features

The design features under study here include interactions, technology with its quality, face-to-face support and learning management system tools and resources.

Research shows that absence of learner interaction causes failure and eventual drop-out in online courses (Willging & Johnson, 2009 ) and the lack of learner connectedness was noted as an internal factor leading to learner drop-out in online courses (Zielinski, 2000 ). It was also noted that learners may not continue in e- and blended learning if they are unable to make friends thereby being disconnected and developing feelings of isolation during their blended learning experiences (Willging & Johnson, 2009). Learners’ Interactions with teachers and peers can make blended learning effective as its absence makes learners withdraw (Astleitner, 2000 ). Loukis, Georgious and Pazalo (2007) noted that learners’ measuring of a system’s quality, reliability and ease of use leads to learning efficiency and can be so in blended learning. Learner success in blended learning may substantially be affected by system functionality (Pituch & Lee, 2006 ) and may lead to failure of such learning initiatives (Shrain, 2012 ). It is therefore important to examine technology quality for ensuring learning effectiveness in blended learning. Tselios, Daskalakis, and Papadopoulou ( 2011 ) investigated learner perceptions after a learning management system use and found out that the actual system use determines the usefulness among users. It is again noted that a system with poor response time cannot be taken to be useful for e-learning and blended learning especially in cases of limited bandwidth (Anderson, 2004 ). In this study, we investigate the use of Moodle and its tools as a function of potential effectiveness of blended learning.

The quality of learning management system content for learners can be a predictor of good performance in e-and blended learning environments and can lead to learner satisfaction. On the whole, poor quality technology yields no satisfaction by users and therefore the quality of technology significantly affects satisfaction (Piccoli, Ahmad, & Ives, 2001 ). Continued navigation through a learning management system increases use and is an indicator of success in blended learning (Delone & McLean, 2003 ). The efficient use of learning management system and its tools improves learning outcomes in e-learning and blended learning environments.

It is noted that learner satisfaction with a learning management system can be an antecedent factor for blended learning effectiveness. Goyal and Tambe ( 2015 ) noted that learners showed an appreciation to Moodle’s contribution in their learning. They showed positivity with it as it improved their understanding of course material (Ahmad & Al-Khanjari, 2011 ). The study by Goyal and Tambe ( 2015 ) used descriptive statistics to indicate improved learning by use of uploaded syllabus and session plans on Moodle. Improved learning is also noted through sharing study material, submitting assignments and using the calendar. Learners in the study found Moodle to be an effective educational tool.

In blended learning set ups, face-to-face experiences form part of the blend and learner positive attitudes to such sessions could mean blended learning effectiveness. A study by Marriot, Marriot, and Selwyn ( 2004 ) showed learners expressing their preference for face-to-face due to its facilitation of social interaction and communication skills acquired from classroom environment. Their preference for the online session was only in as far as it complemented the traditional face-to-face learning. Learners in a study by Osgerby ( 2013 ) had positive perceptions of blended learning but preferred face-to-face with its step-by-stem instruction. Beard, Harper and Riley ( 2004 ) shows that some learners are successful while in a personal interaction with teachers and peers thus prefer face-to-face in the blend. Beard however dealt with a comparison between online and on-campus learning while our study combines both, singling out the face-to-face part of the blend. The advantage found by Beard is all the same relevant here because learners in blended learning express attitude to both online and face-to-face for an effective blend. Researchers indicate that teacher presence in face-to-face sessions lessens psychological distance between them and the learners and leads to greater learning. This is because there are verbal aspects like giving praise, soliciting for viewpoints, humor, etc and non-verbal expressions like eye contact, facial expressions, gestures, etc which make teachers to be closer to learners psychologically (Kelley & Gorham, 2009 ).

Learner outcomes

The outcomes under scrutiny in this study include performance, motivation, satisfaction and knowledge construction. Motivation is seen here as an outcome because, much as cognitive factors such as course grades are used in measuring learning outcomes, affective factors like intrinsic motivation may also be used to indicate outcomes of learning (Kuo, Walker, Belland, & Schroder, 2013 ). Research shows that high motivation among online learners leads to persistence in their courses (Menager-Beeley, 2004 ). Sankaran and Bui ( 2001 ) indicated that less motivated learners performed poorly in knowledge tests while those with high learning motivation demonstrate high performance in academics (Green, Nelson, Martin, & Marsh, 2006 ). Lim and Kim, ( 2003 ) indicated that learner interest as a motivation factor promotes learner involvement in learning and this could lead to learning effectiveness in blended learning.

Learner satisfaction was noted as a strong factor for effectiveness of blended and online courses (Wilging & Johnson, 2009) and dissatisfaction may result from learners’ incompetence in the use of the learning management system as an effective learning tool since, as Islam ( 2014 ) puts it, users may be dissatisfied with an information system due to ease of use. A lack of prompt feedback for learners from course instructors was found to cause dissatisfaction in an online graduate course. In addition, dissatisfaction resulted from technical difficulties as well as ambiguous course instruction Hara and Kling ( 2001 ). These factors, once addressed, can lead to learner satisfaction in e-learning and blended learning and eventual effectiveness. A study by Blocker and Tucker ( 2001 ) also showed that learners had difficulties with technology and inadequate group participation by peers leading to dissatisfaction within these design features. Student-teacher interactions are known to bring satisfaction within online courses. Study results by Swan ( 2001 ) indicated that student-teacher interaction strongly related with student satisfaction and high learner-learner interaction resulted in higher levels of course satisfaction. Descriptive results by Naaj, Nachouki, and Ankit ( 2012 ) showed that learners were satisfied with technology which was a video-conferencing component of blended learning with a mean of 3.7. The same study indicated student satisfaction with instructors at a mean of 3.8. Askar and Altun, ( 2008 ) found that learners were satisfied with face-to-face sessions of the blend with t-tests and ANOVA results indicating female scores as higher than for males in the satisfaction with face-to-face environment of the blended learning.

Studies comparing blended learning with traditional face-to-face have indicated that learners perform equally well in blended learning and their performance is unaffected by the delivery method (Kwak, Menezes, & Sherwood, 2013 ). In another study, learning experience and performance are known to improve when traditional course delivery is integrated with online learning (Stacey & Gerbic, 2007 ). Such improvement as noted may be an indicator of blended learning effectiveness. Our study however, delves into improved performance but seeks to establish the potential of blended learning effectiveness by considering grades obtained in a blended learning experiment. Score 50 and above is considered a pass in this study’s setting and learners scoring this and above will be considered to have passed. This will make our conclusions about the potential of blended learning effectiveness.

Regarding knowledge construction, it has been noted that effective learning occurs where learners are actively involved (Nurmela, Palonen, Lehtinen & Hakkarainen, 2003 , cited in Zhu, 2012 ) and this may be an indicator of learning environment effectiveness. Effective blended learning would require that learners are able to initiate, discover and accomplish the processes of knowledge construction as antecedents of blended learning effectiveness. A study by Rahman, Yasin and Jusoff ( 2011 ) indicated that learners were able to use some steps to construct meaning through an online discussion process through assignments given. In the process of giving and receiving among themselves, the authors noted that learners learned by writing what they understood. From our perspective, this can be considered to be accomplishment in the knowledge construction process. Their study further shows that learners construct meaning individually from assignments and this stage is referred to as pre-construction which for our study, is an aspect of discovery in the knowledge construction process.

Predictors of blended learning effectiveness

Researchers have dealt with success factors for online learning or those for traditional face-to-face learning but little is known about factors that predict blended learning effectiveness in view of learner characteristics and blended learning design features. This part of our study seeks to establish the learner characteristics/backgrounds and design features that predict blended learning effectiveness with regard to satisfaction, outcomes, motivation and knowledge construction. Song, Singleton, Hill, and Koh ( 2004 ) examined online learning effectiveness factors and found out that time management (a self-regulatory factor) was crucial for successful online learning. Eom, Wen, and Ashill ( 2006 ) using a survey found out that interaction, among other factors, was significant for learner satisfaction. Technical problems with regard to instructional design were a challenge to online learners thus not indicating effectiveness (Song et al., 2004 ), though the authors also indicated that descriptive statistics to a tune of 75% and time management (62%) impact on success of online learning. Arbaugh ( 2000 ) and Swan ( 2001 ) indicated that high levels of learner-instructor interaction are associated with high levels of user satisfaction and learning outcomes. A study by Naaj et al. ( 2012 ) indicated that technology and learner interactions, among other factors, influenced learner satisfaction in blended learning.

Objective and research questions of the current study

The objective of the current study is to investigate the effectiveness of blended learning in view of student satisfaction, knowledge construction, performance and intrinsic motivation and how they are related to student characteristics and blended learning design features in a blended learning environment.

Research questions

What are the student characteristics and blended learning design features for an effective blended learning environment?

Which factors (among the learner characteristics and blended learning design features) predict student satisfaction, learning outcomes, intrinsic motivation and knowledge construction?

Conceptual model of the present study

The reviewed literature clearly shows learner characteristics/background and blended learning design features play a part in blended learning effectiveness and some of them are significant predictors of effectiveness. The conceptual model for our study is depicted as follows (Fig.  1 ):

Conceptual model of the current study

Research design

This research applies a quantitative design where descriptive statistics are used for the student characteristics and design features data, t-tests for the age and gender variables to determine if they are significant in blended learning effectiveness and regression for predictors of blended learning effectiveness.

This study is based on an experiment in which learners participated during their study using face-to-face sessions and an on-line session of a blended learning design. A learning management system (Moodle) was used and learner characteristics/background and blended learning design features were measured in relation to learning effectiveness. It is therefore a planning evaluation research design as noted by Guskey ( 2000 ) since the outcomes are aimed at blended learning implementation at MMU. The plan under which the various variables were tested involved face-to-face study at the beginning of a 17 week semester which was followed by online teaching and learning in the second half of the semester. The last part of the semester was for another face-to-face to review work done during the online sessions and final semester examinations. A questionnaire with items on student characteristics, design features and learning outcomes was distributed among students from three schools and one directorate of postgraduate studies.

Participants

Cluster sampling was used to select a total of 238 learners to participate in this study. Out of the whole university population of students, three schools and one directorate were used. From these, one course unit was selected from each school and all the learners following the course unit were surveyed. In the school of Education ( n  = 70) and Business and Management Studies ( n  = 133), sophomore students were involved due to the fact that they have been introduced to ICT basics during their first year of study. Students of the third year were used from the department of technology in the School of Applied Sciences and Technology ( n  = 18) since most of the year two courses had a lot of practical aspects that could not be used for the online learning part. From the Postgraduate Directorate ( n  = 17), first and second year students were selected because learners attend a face-to-face session before they are given paper modules to study away from campus.

The study population comprised of 139 male students representing 58.4% and 99 females representing 41.6% with an average age of 24 years.

Instruments

The end of semester results were used to measure learner performance. The online self-regulated learning questionnaire (Barnard, Lan, To, Paton, & Lai, 2009 ) and the intrinsic motivation inventory (Deci & Ryan, 1982 ) were applied to measure the constructs on self regulation in the student characteristics and motivation in the learning outcome constructs. Other self-developed instruments were used for the other remaining variables of attitudes, computer competence, workload management, social and family support, satisfaction, knowledge construction, technology quality, interactions, learning management system tools and resources and face-to-face support.

Instrument reliability

Cronbach’s alpha was used to test reliability and the table below gives the results. All the scales and sub-scales had acceptable internal consistency reliabilities as shown in Table  1 below:

Data analysis

First, descriptive statistics was conducted. Shapiro-Wilk test was done to test normality of the data for it to qualify for parametric tests. The test results for normality of our data before the t- test resulted into significant levels (Male = .003, female = .000) thereby violating the normality assumption. We therefore used the skewness and curtosis results which were between −1.0 and +1.0 and assumed distribution to be sufficiently normal to qualify the data for a parametric test, (Pallant, 2010 ). An independent samples t -test was done to find out the differences in male and female performance to explain the gender characteristics in blended learning effectiveness. A one-way ANOVA between subjects was conducted to establish the differences in performance between age groups. Finally, multiple regression analysis was done between student variables and design elements with learning outcomes to determine the significant predictors for blended learning effectiveness.

Student characteristics, blended learning design features and learning outcomes ( RQ1 )

A t- test was carried out to establish the performance of male and female learners in the blended learning set up. This was aimed at finding out if male and female learners do perform equally well in blended learning given their different roles and responsibilities in society. It was found that male learners performed slightly better ( M  = 62.5) than their female counterparts ( M  = 61.1). An independent t -test revealed that the difference between the performances was not statistically significant ( t  = 1.569, df = 228, p  = 0.05, one tailed). The magnitude of the differences in the means is small with effect size ( d  = 0.18). A one way between subjects ANOVA was conducted on the performance of different age groups to establish the performance of learners of young and middle aged age groups (20–30, young & and 31–39, middle aged). This revealed a significant difference in performance (F(1,236 = 8.498, p < . 001).

Average percentages of the items making up the self regulated learning scale are used to report the findings about all the sub-scales in the learner characteristics/background scale. Results show that learner self-regulation was good enough at 72.3% in all the sub-scales of goal setting, environment structuring, task strategies, time management, help-seeking and self-evaluation among learners. The least in the scoring was task strategies at 67.7% and the highest was learner environment structuring at 76.3%. Learner attitude towards blended learning environment is at 76% in the sub-scales of learner autonomy, quality of instructional materials, course structure, course interface and interactions. The least scored here is attitude to course structure at 66% and their attitudes were high on learner autonomy and course interface both at 82%. Results on the learners’ computer competences are summarized in percentages in the table below (Table  2 ):

It can be seen that learners are skilled in word processing at 91%, email at 63.5%, spreadsheets at 68%, web browsers at 70.2% and html tools at 45.4%. They are therefore good enough in word processing and web browsing. Their computer confidence levels are reported at 75.3% and specifically feel very confident when it comes to working with a computer (85.7%). Levels of family and social support for learners during blended learning experiences are at 60.5 and 75% respectively. There is however a low score on learners being assisted by family members in situations of computer setbacks (33.2%) as 53.4% of the learners reported no assistance in this regard. A higher percentage (85.3%) is reported on learners getting support from family regarding provision of essentials for learning such as tuition. A big percentage of learners spend two hours on study while at home (35.3%) followed by one hour (28.2%) while only 9.7% spend more than three hours on study at home. Peers showed great care during the blended learning experience (81%) and their experiences were appreciated by the society (66%). Workload management by learners vis-à-vis studying is good at 60%. Learners reported that their workmates stand in for them at workplaces to enable them do their study in blended learning while 61% are encouraged by their bosses to go and improve their skills through further education and training. On the time spent on other activities not related to study, majority of the learners spend three hours (35%) while 19% spend 6 hours. Sixty percent of the learners have to answer to someone when they are not attending to other activities outside study compared to the 39.9% who do not and can therefore do study or those other activities.

The usability of the online system, tools and resources was below average as shown in the table below in percentages (Table  3 ):

However, learners became skilled at navigating around the learning management system (79%) and it was easy for them to locate course content, tools and resources needed such as course works, news, discussions and journal materials. They effectively used the communication tools (60%) and to work with peers by making posts (57%). They reported that online resources were well organized, user friendly and easy to access (71%) as well as well structured in a clear and understandable manner (72%). They therefore recommended the use of online resources for other course units in future (78%) because they were satisfied with them (64.3%). On the whole, the online resources were fine for the learners (67.2%) and useful as a learning resource (80%). The learners’ perceived usefulness/satisfaction with online system, tools, and resources was at 81% as the LMS tools helped them to communicate, work with peers and reflect on their learning (74%). They reported that using moodle helped them to learn new concepts, information and gaining skills (85.3%) as well as sharing what they knew or learned (76.4%). They enjoyed the course units (78%) and improved their skills with technology (89%).

Learner interactions were seen from three angles of cognitivism, collaborative learning and student-teacher interactions. Collaborative learning was average at 50% with low percentages in learners posting challenges to colleagues’ ideas online (34%) and posting ideas for colleagues to read online (37%). They however met oftentimes online (60%) and organized how they would work together in study during the face-to-face meetings (69%). The common form of communication medium frequently used by learners during the blended learning experience was by phone (34.5%) followed by whatsapp (21.8%), face book (21%), discussion board (11.8%) and email (10.9%). At the cognitive level, learners interacted with content at 72% by reading the posted content (81%), exchanging knowledge via the LMS (58.4%), participating in discussions on the forum (62%) and got course objectives and structure introduced during the face-to-face sessions (86%). Student-teacher interaction was reported at 71% through instructors individually working with them online (57.2%) and being well guided towards learning goals (81%). They did receive suggestions from instructors about resources to use in their learning (75.3%) and instructors provided learning input for them to come up with their own answers (71%).

The technology quality during the blended learning intervention was rated at 69% with availability of 72%, quality of the resources was at 68% with learners reporting that discussion boards gave right content necessary for study (71%) and the email exchanges containing relevant and much needed information (63.4%) as well as chats comprising of essential information to aid the learning (69%). Internet reliability was rated at 66% with a speed considered averagely good to facilitate online activities (63%). They however reported that there was intermittent breakdown during online study (67%) though they could complete their internet program during connection (63.4%). Learners eventually found it easy to download necessary materials for study in their blended learning experiences (71%).

Learner extent of use of the learning management system features was as shown in the table below in percentage (Table  4 ):

From the table, very rarely used features include the blog and wiki while very often used ones include the email, forum, chat and calendar.

The effectiveness of the LMS was rated at 79% by learners reporting that they found it useful (89%) and using it makes their learning activities much easier (75.2%). Moodle has helped learners to accomplish their learning tasks more quickly (74%) and that as a LMS, it is effective in teaching and learning (88%) with overall satisfaction levels at 68%. However, learners note challenges in the use of the LMS regarding its performance as having been problematic to them (57%) and only 8% of the learners reported navigation while 16% reported access as challenges.

Learner attitudes towards Face-to-face support were reported at 88% showing that the sessions were enjoyable experiences (89%) with high quality class discussions (86%) and therefore recommended that the sessions should continue in blended learning (89%). The frequency of the face-to-face sessions is shown in the table below as preferred by learners (Table  5 ).

Learners preferred face-to-face sessions after every month in the semester (33.6%) and at the beginning of the blended learning session only (27.7%).

Learners reported high intrinsic motivation levels with interest and enjoyment of tasks at 83.7%, perceived competence at 70.2%, effort/importance sub-scale at 80%, pressure/tension reported at 54%. The pressure percentage of 54% arises from learners feeling nervous (39.2%) and a lot of anxiety (53%) while 44% felt a lot of pressure during the blended learning experiences. Learners however reported the value/usefulness of blended learning at 91% with majority believing that studying online and face-to-face had value for them (93.3%) and were therefore willing to take part in blended learning (91.2%). They showed that it is beneficial for them (94%) and that it was an important way of studying (84.3%).

Learner satisfaction was reported at 81% especially with instructors (85%) high percentage reported on encouraging learner participation during the course of study 93%, course content (83%) with the highest being satisfaction with the good relationship between the objectives of the course units and the content (90%), technology (71%) with a high percentage on the fact that the platform was adequate for the online part of the learning (76%), interactions (75%) with participation in class at 79%, and face-to-face sessions (91%) with learner satisfaction high on face-to-face sessions being good enough for interaction and giving an overview of the courses when objectives were introduced at 92%.

Learners’ knowledge construction was reported at 78% with initiation and discovery scales scoring 84% with 88% specifically for discovering the learning points in the course units. The accomplishment scale in knowledge construction scored 71% and specifically the fact that learners were able to work together with group members to accomplish learning tasks throughout the study of the course units (79%). Learners developed reports from activities (67%), submitted solutions to discussion questions (68%) and did critique peer arguments (69%). Generally, learners performed well in blended learning in the final examination with an average pass of 62% and standard deviation of 7.5.

Significant predictors of blended learning effectiveness ( RQ 2)

A standard multiple regression analysis was done taking learner characteristics/background and design features as predictor variables and learning outcomes as criterion variables. The data was first tested to check if it met the linear regression test assumptions and results showed the correlations between the independent variables and each of the dependent variables (highest 0.62 and lowest 0.22) as not being too high, which indicated that multicollinearity was not a problem in our model. From the coefficients table, the VIF values ranged from 1.0 to 2.4, well below the cut off value of 10 and indicating no possibility of multicollinearity. The normal probability plot was seen to lie as a reasonably straight diagonal from bottom left to top right indicating normality of our data. Linearity was found suitable from the scatter plot of the standardized residuals and was rectangular in distribution. Outliers were no cause for concern in our data since we had only 1% of all cases falling outside 3.0 thus proving the data as a normally distributed sample. Our R -square values was at 0.525 meaning that the independent variables explained about 53% of the variance in overall satisfaction, motivation and knowledge construction of the learners. All the models explaining the three dependent variables of learner satisfaction, intrinsic motivation and knowledge construction were significant at the 0.000 probability level (Table  6 ).

From the table above, design features (technology quality and online tools and resources), and learner characteristics (attitudes to blended learning, self-regulation) were significant predictors of learner satisfaction in blended learning. This means that good technology with the features involved and the learner positive attitudes with capacity to do blended learning with self drive led to their satisfaction. The design features (technology quality, interactions) and learner characteristics (self regulation and social support), were found to be significant predictors of learner knowledge construction. This implies that learners’ capacity to go on their work by themselves supported by peers and high levels of interaction using the quality technology led them to construct their own ideas in blended learning. Design features (technology quality, online tools and resources as well as learner interactions) and learner characteristics (self regulation), significantly predicted the learners’ intrinsic motivation in blended learning suggesting that good technology, tools and high interaction levels with independence in learning led to learners being highly motivated. Finally, none of the independent variables considered under this study were predictors of learning outcomes (grade).

In this study we have investigated learning outcomes as dependent variables to establish if particular learner characteristics/backgrounds and design features are related to the outcomes for blended learning effectiveness and if they predict learning outcomes in blended learning. We took students from three schools out of five and one directorate of post-graduate studies at a Ugandan University. The study suggests that the characteristics and design features examined are good drivers towards an effective blended learning environment though a few of them predicted learning outcomes in blended learning.

Student characteristics/background, blended learning design features and learning outcomes

The learner characteristics, design features investigated are potentially important for an effective blended learning environment. Performance by gender shows a balance with no statistical differences between male and female. There are statistically significant differences ( p  < .005) in the performance between age groups with means of 62% for age group 20–30 and 67% for age group 31 –39. The indicators of self regulation exist as well as positive attitudes towards blended learning. Learners do well with word processing, e-mail, spreadsheets and web browsers but still lag below average in html tools. They show computer confidence at 75.3%; which gives prospects for an effective blended learning environment in regard to their computer competence and confidence. The levels of family and social support for learners stand at 61 and 75% respectively, indicating potential for blended learning to be effective. The learners’ balance between study and work is a drive factor towards blended learning effectiveness since their management of their workload vis a vis study time is at 60 and 61% of the learners are encouraged to go for study by their bosses. Learner satisfaction with the online system and its tools shows prospect for blended learning effectiveness but there are challenges in regard to locating course content and assignments, submitting their work and staying on a task during online study. Average collaborative, cognitive learning as well as learner-teacher interactions exist as important factors. Technology quality for effective blended learning is a potential for effectiveness though features like the blog and wiki are rarely used by learners. Face-to-face support is satisfactory and it should be conducted every month. There is high intrinsic motivation, satisfaction and knowledge construction as well as good performance in examinations ( M  = 62%, SD = 7.5); which indicates potentiality for blended learning effectiveness.

Significant predictors of blended learning effectiveness

Among the design features, technology quality, online tools and face-to-face support are predictors of learner satisfaction while learner characteristics of self regulation and attitudes to blended learning are predictors of satisfaction. Technology quality and interactions are the only design features predicting learner knowledge construction, while social support, among the learner backgrounds, is a predictor of knowledge construction. Self regulation as a learner characteristic is a predictor of knowledge construction. Self regulation is the only learner characteristic predicting intrinsic motivation in blended learning while technology quality, online tools and interactions are the design features predicting intrinsic motivation. However, all the independent variables are not significant predictors of learning performance in blended learning.

The high computer competences and confidence is an antecedent factor for blended learning effectiveness as noted by Hadad ( 2007 ) and this study finds learners confident and competent enough for the effectiveness of blended learning. A lack in computer skills causes failure in e-learning and blended learning as noted by Shraim and Khlaif ( 2010 ). From our study findings, this is no threat for blended learning our case as noted by our results. Contrary to Cohen et al. ( 2012 ) findings that learners’ family responsibilities and hours of employment can impede their process of learning, it is not the case here since they are drivers to the blended learning process. Time conflict, as compounded by family, employment status and management support (Packham et al., 2004 ) were noted as causes of learner failure and drop out of online courses. Our results show, on the contrary, that these factors are drivers for blended learning effectiveness because learners have a good balance between work and study and are supported by bosses to study. In agreement with Selim ( 2007 ), learner positive attitudes towards e-and blended learning environments are success factors. In line with Coldwell et al. ( 2008 ), no statistically significant differences exist between age groups. We however note that Coldwel, et al dealt with young, middle-aged and old above 45 years whereas we dealt with young and middle aged only.

Learner interactions at all levels are good enough and contrary to Astleitner, ( 2000 ) that their absence makes learners withdraw, they are a drive factor here. In line with Loukis (2007) the LMS quality, reliability and ease of use lead to learning efficiency as technology quality, online tools are predictors of learner satisfaction and intrinsic motivation. Face-to-face sessions should continue on a monthly basis as noted here and is in agreement with Marriot et al. ( 2004 ) who noted learner preference for it for facilitating social interaction and communication skills. High learner intrinsic motivation leads to persistence in online courses as noted by Menager-Beeley, ( 2004 ) and is high enough in our study. This implies a possibility of an effectiveness blended learning environment. The causes of learner dissatisfaction noted by Islam ( 2014 ) such as incompetence in the use of the LMS are contrary to our results in our study, while the one noted by Hara and Kling, ( 2001 ) as resulting from technical difficulties and ambiguous course instruction are no threat from our findings. Student-teacher interaction showed a relation with satisfaction according to Swan ( 2001 ) but is not a predictor in our study. Initiating knowledge construction by learners for blended learning effectiveness is exhibited in our findings and agrees with Rahman, Yasin and Jusof ( 2011 ). Our study has not agreed with Eom et al. ( 2006 ) who found learner interactions as predictors of learner satisfaction but agrees with Naaj et al. ( 2012 ) regarding technology as a predictor of learner satisfaction.

Conclusion and recommendations

An effective blended learning environment is necessary in undertaking innovative pedagogical approaches through the use of technology in teaching and learning. An examination of learner characteristics/background, design features and learning outcomes as factors for effectiveness can help to inform the design of effective learning environments that involve face-to-face sessions and online aspects. Most of the student characteristics and blended learning design features dealt with in this study are important factors for blended learning effectiveness. None of the independent variables were identified as significant predictors of student performance. These gaps are open for further investigation in order to understand if they can be significant predictors of blended learning effectiveness in a similar or different learning setting.

In planning to design and implement blended learning, we are mindful of the implications raised by this study which is a planning evaluation research for the design and eventual implementation of blended learning. Universities should be mindful of the interplay between the learner characteristics, design features and learning outcomes which are indicators of blended learning effectiveness. From this research, learners manifest high potential to take on blended learning more especially in regard to learner self-regulation exhibited. Blended learning is meant to increase learners’ levels of knowledge construction in order to create analytical skills in them. Learner ability to assess and critically evaluate knowledge sources is hereby established in our findings. This can go a long way in producing skilled learners who can be innovative graduates enough to satisfy employment demands through creativity and innovativeness. Technology being less of a shock to students gives potential for blended learning design. Universities and other institutions of learning should continue to emphasize blended learning approaches through installation of learning management systems along with strong internet to enable effective learning through technology especially in the developing world.

Abubakar, D. & Adetimirin. (2015). Influence of computer literacy on post-graduates’ use of e-resources in Nigerian University Libraries. Library Philosophy and Practice. From http://digitalcommons.unl.edu/libphilprac/ . Retrieved 18 Aug 2015.

Ahmad, N., & Al-Khanjari, Z. (2011). Effect of Moodle on learning: An Oman perception. International Journal of Digital Information and Wireless Communications (IJDIWC), 1 (4), 746–752.

Google Scholar  

Anderson, T. (2004). Theory and Practice of Online Learning . Canada: AU Press, Athabasca University.

Arbaugh, J. B. (2000). How classroom environment and student engagement affect learning in internet-basedMBAcourses. Business Communication Quarterly, 63 (4), 9–18.

Article   Google Scholar  

Askar, P. & Altun, A. (2008). Learner satisfaction on blended learning. E-Leader Krakow , 2008.

Astleitner, H. (2000) Dropout and distance education. A review of motivational and emotional strategies to reduce dropout in web-based distance education. In Neuwe Medien in Unterricht, Aus-und Weiterbildung Waxmann Munster, New York.

Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O., & Lai, S. (2009). Measuring self regulation in online and blended learning environments’. Internet and Higher Education, 12 (1), 1–6.

Beard, L. A., Harper, C., & Riley, G. (2004). Online versus on-campus instruction: student attitudes & perceptions. TechTrends, 48 (6), 29–31.

Berenson, R., Boyles, G., & Weaver, A. (2008). Emotional intelligence as a predictor for success in online learning. International Review of Research in open & Distance Learning, 9 (2), 1–16.

Blocker, J. M., & Tucker, G. (2001). Using constructivist principles in designing and integrating online collaborative interactions. In F. Fuller & R. McBride (Eds.), Distance education. Proceedings of the Society for Information Technology & Teacher Education International Conference (pp. 32–36). ERIC Document Reproduction Service No. ED 457 822.

Cohen, K. E., Stage, F. K., Hammack, F. M., & Marcus, A. (2012). Persistence of master’s students in the United States: Developing and testing of a conceptual model . USA: PhD Dissertation, New York University.

Coldwell, J., Craig, A., Paterson, T., & Mustard, J. (2008). Online students: Relationships between participation, demographics and academic performance. The Electronic Journal of e-learning, 6 (1), 19–30.

Deci, E. L., & Ryan, R. M. (1982). Intrinsic Motivation Inventory. Available from selfdeterminationtheory.org/intrinsic-motivation-inventory/ . Accessed 2 Aug 2016.

Delone, W. H., & McLean, E. R. (2003). The Delone and McLean model of information systems success: A Ten-year update. Journal of Management Information Systems, 19 (4), 9–30.

Demirkol, M., & Kazu, I. Y. (2014). Effect of blended environment model on high school students’ academic achievement. The Turkish Online Journal of Educational Technology, 13 (1), 78–87.

Eom, S., Wen, H., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: an empirical investigation’. Decision Sciences Journal of Innovative Education, 4 (2), 215–235.

Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. Internet and Higher Education, 7 (2), 95–105.

Goyal, E., & Tambe, S. (2015). Effectiveness of Moodle-enabled blended learning in private Indian Business School teaching NICHE programs. The Online Journal of New Horizons in Education, 5 (2), 14–22.

Green, J., Nelson, G., Martin, A. J., & Marsh, H. (2006). The causal ordering of self-concept and academic motivation and its effect on academic achievement. International Education Journal, 7 (4), 534–546.

Guskey, T. R. (2000). Evaluating Professional Development . Thousands Oaks: Corwin Press.

Hadad, W. (2007). ICT-in-education toolkit reference handbook . InfoDev. from http://www.infodev.org/en/Publication.301.html . Retrieved 04 Aug 2015.

Hara, N. & Kling, R. (2001). Student distress in web-based distance education. Educause Quarterly. 3 (2001).

Heinich, R., Molenda, M., Russell, J. D., & Smaldino, S. E. (2001). Instructional Media and Technologies for Learning (7th ed.). Englewood Cliffs: Prentice-Hall.

Hofmann, J. (2014). Solutions to the top 10 challenges of blended learning. Top 10 challenges of blended learning. Available on cedma-europe.org .

Islam, A. K. M. N. (2014). Sources of satisfaction and dissatisfaction with a learning management system in post-adoption stage: A critical incident technique approach. Computers in Human Behaviour, 30 , 249–261.

Kelley, D. H. & Gorham, J. (2009) Effects of immediacy on recall of information. Communication Education, 37 (3), 198–207.

Kenney, J., & Newcombe, E. (2011). Adopting a blended learning approach: Challenges, encountered and lessons learned in an action research study. Journal of Asynchronous Learning Networks, 15 (1), 45–57.

Kintu, M. J., & Zhu, C. (2016). Student characteristics and learning outcomes in a blended learning environment intervention in a Ugandan University. Electronic Journal of e-Learning, 14 (3), 181–195.

Kuo, Y., Walker, A. E., Belland, B. R., & Schroder, L. E. E. (2013). A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distributed Learning, 14 (1), 16–39.

Kwak, D. W., Menezes, F. M., & Sherwood, C. (2013). Assessing the impact of blended learning on student performance. Educational Technology & Society, 15 (1), 127–136.

Lim, D. H., & Kim, H. J. (2003). Motivation and learner characteristics affecting online learning and learning application. Journal of Educational Technology Systems, 31 (4), 423–439.

Lim, D. H., & Morris, M. L. (2009). Learner and instructional factors influencing learner outcomes within a blended learning environment. Educational Technology & Society, 12 (4), 282–293.

Lin, B., & Vassar, J. A. (2009). Determinants for success in online learning communities. International Journal of Web-based Communities, 5 (3), 340–350.

Loukis, E., Georgiou, S. & Pazalo, K. (2007). A value flow model for the evaluation of an e-learning service. ECIS, 2007 Proceedings, paper 175.

Lynch, R., & Dembo, M. (2004). The relationship between self regulation and online learning in a blended learning context. The International Review of Research in Open and Distributed Learning, 5 (2), 1–16.

Marriot, N., Marriot, P., & Selwyn. (2004). Accounting undergraduates’ changing use of ICT and their views on using the internet in higher education-A Research note. Accounting Education, 13 (4), 117–130.

Menager-Beeley, R. (2004). Web-based distance learning in a community college: The influence of task values on task choice, retention and commitment. (Doctoral dissertation, University of Southern California). Dissertation Abstracts International, 64 (9-A), 3191.

Naaj, M. A., Nachouki, M., & Ankit, A. (2012). Evaluating student satisfaction with blended learning in a gender-segregated environment. Journal of Information Technology Education: Research, 11 , 185–200.

Nurmela, K., Palonen, T., Lehtinen, E. & Hakkarainen, K. (2003). Developing tools for analysing CSCL process. In Wasson, B. Ludvigsen, S. & Hoppe, V. (eds), Designing for change in networked learning environments (pp 333–342). Dordrecht, The Netherlands, Kluwer.

Osgerby, J. (2013). Students’ perceptions of the introduction of a blended learning environment: An exploratory case study. Accounting Education, 22 (1), 85–99.

Oxford Group, (2013). Blended learning-current use, challenges and best practices. From http://www.kineo.com/m/0/blended-learning-report-202013.pdf . Accessed on 17 Mar 2016.

Packham, G., Jones, P., Miller, C., & Thomas, B. (2004). E-learning and retention key factors influencing student withdrawal. Education and Training, 46 (6–7), 335–342.

Pallant, J. (2010). SPSS Survival Mannual (4th ed.). Maidenhead: OUP McGraw-Hill.

Park, J.-H., & Choi, H. J. (2009). Factors influencing adult learners’ decision to drop out or persist in online learning. Educational Technology & Society, 12 (4), 207–217.

Picciano, A., & Seaman, J. (2007). K-12 online learning: A survey of U.S. school district administrators . New York, USA: Sloan-C.

Piccoli, G., Ahmad, R., & Ives, B. (2001). Web-based virtual learning environments: a research framework and a preliminary assessment of effectiveness in basic IT skill training. MIS Quarterly, 25 (4), 401–426.

Pituch, K. A., & Lee, Y. K. (2006). The influence of system characteristics on e-learning use. Computers & Education, 47 (2), 222–244.

Rahman, S. et al, (2011). Knowledge construction process in online learning. Middle East Journal of Scientific Research, 8 (2), 488–492.

Rovai, A. P. (2003). In search of higher persistence rates in distance education online programs. Computers & Education, 6 (1), 1–16.

Sankaran, S., & Bui, T. (2001). Impact of learning strategies and motivation on performance: A study in Web-based instruction. Journal of Instructional Psychology, 28 (3), 191–198.

Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. Computers & Education, 49 (2), 396–413.

Shraim, K., & Khlaif, Z. N. (2010). An e-learning approach to secondary education in Palestine: opportunities and challenges. Information Technology for Development, 16 (3), 159–173.

Shrain, K. (2012). Moving towards e-learning paradigm: Readiness of higher education instructors in Palestine. International Journal on E-Learning, 11 (4), 441–463.

Song, L., Singleton, E. S., Hill, J. R., & Koh, M. H. (2004). Improving online learning: student perceptions of useful and challenging characteristics’. Internet and Higher Education, 7 (1), 59–70.

Stacey, E., & Gerbic, P. (2007). Teaching for blended learning: research perspectives from on-campus and distance students. Education and Information Technologies, 12 , 165–174.

Swan, K. (2001). Virtual interactivity: design factors affecting student satisfaction and perceived learning in asynchronous online courses. Distance Education, 22 (2), 306–331.

Article   MathSciNet   Google Scholar  

Thompson, E. (2004). Distance education drop-out: What can we do? In R. Pospisil & L. Willcoxson (Eds.), Learning Through Teaching (Proceedings of the 6th Annual Teaching Learning Forum, pp. 324–332). Perth, Australia: Murdoch University.

Tselios, N., Daskalakis, S., & Papadopoulou, M. (2011). Assessing the acceptance of a blended learning university course. Educational Technology & Society, 14 (2), 224–235.

Willging, P. A., & Johnson, S. D. (2009). Factors that influence students’ decision to drop-out of online courses. Journal of Asynchronous Learning Networks, 13 (3), 115–127.

Zhu, C. (2012). Student satisfaction, performance and knowledge construction in online collaborative learning. Educational Technology & Society, 15 (1), 127–137.

Zielinski, D. (2000). Can you keep learners online? Training, 37 (3), 64–75.

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MJK conceived the study idea, developed the conceptual framework, collected the data, analyzed it and wrote the article. CZ gave the technical advice concerning the write-up and advised on relevant corrections to be made before final submission. EK did the proof-reading of the article as well as language editing. All authors read and approved the final manuscript.

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Kintu, M.J., Zhu, C. & Kagambe, E. Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. Int J Educ Technol High Educ 14 , 7 (2017). https://doi.org/10.1186/s41239-017-0043-4

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The Research Alliance for New York City Schools

Exploring the Evidence on Virtual and Blended Learning

Chelsea farley (2020).

The Research Alliance has developed an overview of research and practical guidance on strategies to implement remote teaching and learning, as well as strategies that combine virtual and in-class instruction. While not a complete summary of the relevant literature, our overview provides links to a variety of useful articles, resources, and reports. We hope this material can inform school and district leaders’ planning and support their ongoing assessment of what has and has not been effective, for whom, and under what conditions.

Key Takeaways from the Research Alliance’s Review

• Eight months into the COVID-19 pandemic, there is still an enormous need for data and evidence to understand how the school closures that took place in NYC and around the country—and how the various approaches to reopening—have affected students’ academic, social/emotional, and health outcomes. New research is needed to inform critical policy and practice decisions. (Below we highlight specific kinds of data that would help answer the most pressing questions.)
• Past research about online learning is limited and mostly focused on post-secondary and adult education. The studies that do exist in K-12 education find that students participating in online learning generally perform similarly to or worse than peers who have access to traditional face-to-face instruction (with programs that are 100% online faring worse than blended learning approaches). It is important to note that this research typically compares online learning with regular classroom instruction—rather than comparing it to no instruction at all—and that these studies took place under dramatically different conditions than those resulting from COVID-19.
• Studies of blended learning, personalized learning, and specific technology-based tools and programs provide hints about successful approaches, but also underscore substantial “fuzziness” around the definition of these terms; major challenges to high-quality implementation; and a lack of rigorous impact research.
• Teaching quality is more important than how lessons are delivered  (e.g., “clear explanations, scaffolding and feedback”);
• Ensuring access to technology is key , particularly for disadvantaged students and families;
• Peer interactions can provide motivation and improve learning outcomes  (e.g., “peer marking and feedback, sharing models of good work,” and opportunities for collaboration and live discussions of content);
• Supporting students to work independently can improve learning outcomes  (e.g., “prompting pupils to reflect on their work or to consider the strategies they will use if they get stuck”, checklists or daily plans); and
• Different approaches to remote learning suit different tasks and types of content.

Our overview highlights these and other lessons from dozens of relevant studies. It also underscores the need for more rigorous evidence about the implementation and impact of different approaches to remote and blended learning, particularly in the context of the current pandemic. To begin to fill these knowledge gaps,  the Research Alliance strongly encourages schools and districts—including the NYC Department of Education—to collect, analyze, and share data about :

• COVID-19 testing results,
• Professional development aimed at helping teachers implement remote and blended learning,
• Students’ attendance and engagement (online and in person),
• Students’ social and emotional wellbeing,
• Students’ and families’ experiences with remote and blended instruction,
• Teachers’ experiences with remote and blended instruction, and—critically—
• What students are learning, over time.

All of this should be done with an eye toward pre-existing inequalities—especially differences related to race/ethnicity, poverty, home language, and disability. These data are crucial for understanding how COVID-19 has affected the educational trajectories of different groups of students and for developing strong policy and practice responses. 

Read our full overview here . This document was initially released in May and updated in November of 2020.

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Review article, blended learning in higher education: diversifying models and practical recommendations for researchers.

• 1 Department of Mechanical Engineering, North-Eastern Federal University, Yakutsk, Russia
• 2 Department of Humanities, Financial University Under the Government of the Russian Federation, Moscow, Russia
• 3 Director of Science Center “Intellectual Nation,” Abai Kazakh National Pedagogical University, Almaty, Kazakhstan
• 4 Head of the Department of Theory and History of Journalism, Peoples’ Friendship University of Russia (RUDN University), Moscow, Russia
• 5 Department of State and Legal Disciplines, Plekhanov Russian University of Economics, Moscow, Russia
• 6 Independent Researcher, Gaziantep, Turkey

Blended learning is gaining popularity because it has shown to be a successful method for accommodating an increasingly varied student body while enhancing the learning environment by incorporating online teaching materials. Higher education research on blended learning contributes to the blended learning literature. The ideas for future researchers are a vital component of research-based research articles. This study aims to consolidate the recommendations made for future studies. Research articles published in Scope-indexed journals over the past 5 years were analyzed in this context. Each cited passage from the research was read and coded independently in this analysis. After a period of time, the codes were merged into categories and themes. In the results section, direct citations were used to support the codes. The number of publications increased starting in 2017 and continuing through 2020. In the year 2020, most articles were published. Approximately half of the publications provide recommendations for future research. The researchers’ recommendations were gathered under the titles “Research Content” and “Replication and Method” the researchers’ recommendations were gathered.

Introduction

Definition of blended learning.

In November 2002, a few colleagues attending the Annual Sloan-C Conference on Online Learning in Orlando, Florida, discussed a novel phenomenon: college teachers combining face-to-face and online learning strategies and resources in their classrooms ( Picciano et al., 2014 ) was. Blended learning, also known as hybrid learning or mixed-mode education, is an instructional approach that combines the use of one or two different learning methodologies with the more conventional model of instruction in a classroom setting ( Graham, 2006 ; Lee et al., 2017 ; Thai et al., 2017 ; Vasyura et al., 2020 ). Improving data analysis and computation skills has contributed to the popularity of the blended learning instructional style ( Lu et al., 2018 ). Integration of face-to-face learning experiences in the classroom with online learning experiences in a thoughtful manner ( Garrison and Kanuka, 2004 ). Learners engage in collaborative activities utilizing various online and offline resources in a mixed learning environment. Many different models of convergence between technologically enabled settings and more conventional ones, such as virtual labs, have been proposed ( De Jong et al., 2013 ). Graham (2013) examined the many definitions of blended learning. He concluded that the word is most frequently used to refer to the practice of combining traditional face-to-face education with online learning.

Current State in Blended Learning

There are several recommendations available online on the appropriate face-to-face interaction ratio. For instance, 50 % of teaching can be completed online and 50 % in person ( Bernard et al., 2009 ). However, Allen et al. (2007) suggest that the percentage of online classes should be anywhere between 30 and 79 %. Also, experts recommend a blending ratio of 60 % e-Learning and 40 % face-to-face learning for blended learning ( Banyen et al., 2016 ).

On college and university campuses, the use of blended learning as a method of instruction is experiencing rapid growth ( Bernard et al., 2009 ; Porter et al., 2014 ; ElSayary, 2021 ; Chen, 2022 ). Researchers have carried out implementation and study with the presumption that the blended learning application offers various advantages. They used blended learning in higher education studies ( Suleri and Suleri, 2018 ).

These blended approaches encourage both individual learning and cooperation ( Lim and Wang, 2016 ; Talan and Gulsecen, 2019 ) and enable more channels of communication among students as well as between students and their teachers ( McCutcheon et al., 2018 ; Shu and Gu, 2018 ). Blended learning classes offer a unique environment in which to analyze the level of involvement shown by students ( Hasanah and Malik, 2020 ). For students to successfully engage in the online components of the course, they will need to develop skills for navigating the various modalities of teaching and increasing their self-motivation level ( Norberg et al., 2011 ; Baragash and Al-Samarraie, 2018 ; Bervell et al., 2020 ). It is believed that blended learning is a significant factor in determining academic achievement ( Bernard et al., 2009 ; Means et al., 2013 ), student satisfaction ( Zeqiri and Alserhan, 2021 ), and student retention rates ( Pye et al., 2015 ).

It has been voiced in different studies ( Cortez, 2020 ; de Brito Lima et al., 2021 ) that there is a “new normal” in many educational institutions and disciplines after COVID-19 and that blended learning approach has gained serious popularity in this context.

Blended learning preserves student-teacher connection and peer learning. Still, it also can be more adaptable because students may access a portion of their coursework online and the amount of time they need to spend in the classroom can be reduced ( Phillips et al., 2016 ).

Some students have voiced issues ( Maarop and Embi, 2016 ) with the design of courses that combine online with in-class delivery, although blended learning is appealing to institutions and has unrealized potential ( Wang et al., 2015 ; Andreev et al., 2022 ). Blended learning courses combine online with in-class delivery ( Bruff et al., 2013 ; Medina, 2018 ; Smolyaninova et al., 2021 ). Data indicates that the amount of student accomplishment influences the degree to which one is satisfied with blended learning ( Owston et al., 2013 ; Fisher et al., 2017 ).

The Importance of Recommendation for Future Studies

In addition to carrying out research responsibly, accurately reporting its findings is also an essential step ( Pruzan, 2016 ). The fact that suggestions for future researchers are written in method books is considered important in terms of the research’s quality and its Contribution To The Field. According to Sahu (2013) , it is reasonable to anticipate that a successful research program will pave the way for many subsequent research initiatives. Each research report has a portion that focuses on how to expand or continue the current research program to shed more light on the knowledge base and resolve other connected programs that are working along the same lines as the recent research activity ( Belonovskaya et al., 2021 ). A good researcher should also discuss in this part what limitations or gaps exist in the current study program and how these limitations might be solved in a future research program ( Sahu, 2013 ). An essential component of the study report is the acceptance of suggestions, which indicate how the quality of future work may be enhanced and new routes for the continuation of research. These kinds of remarks can inspire ideas for additional study, point out areas that need to be addressed to improve the subject and serve as a valuable roadmap for rookie and expert researchers. An indication to the reader that the author has finished one stage of the research process and is contemplating moving on to the next step is for the author to state directions for future study in the conclusion of the research that is being prepared for publication ( Mackey and Gass, 2015 ). No study was discovered in examining the literature that investigated the suggestion for future research portions of the studies on the subject of blended learning. Due to this, the previous research section could not be mentioned in the study. This work will contribute significantly in terms of offering a collective suggestion to future scholars on the subject of blended learning. In addition, it contributes to the research methodology by restating in broad terms the significance of the content of “recommendations for future researchers.”

In light of this, the purpose of this study was to investigate the recommendations made for further research in the publications that have been published during the past 5 years on blended learning in higher education.

Materials and Methods

This study may be categorized as a qualitative study since it is based on qualitative data analysis on data that was already published in other studies. As a result of the fact that the bibliometric information for the publications received throughout the study is also investigated, this information may also be assessed as part of the bibliometric study.

Data Collection

Scopus, one of the most widely used databases, was chosen to collect data. Scopus aids the research workflow’s efficacy and efficiency ( Why choose Scopus - Scopus benefits | Elsevier solutions, 2018 ). Scopus was selected as the database of choice since it indexes the top journals in the field of education and offers the necessary data for bibliometric research. “Blended Learning” and “Higher Education” were used as the study’s search keys. In the study, the last 5 (2017–2021) years and the conditions of being a published article were added. As a result of the first search, 2657 articles were obtained. Since the publications will be included in the content analysis, the restriction that the broadcast language is English has been added. As a result of the search, 1958 articles were identified. The obtained data were downloaded in CVS format for analysis.

As seen in Figure 1 , the selection and elimination process of the publications has been started. The 1958 article was primarily examined for duplication. Nine articles that did not meet the requirement were excluded from the study. The titles and abstracts of the 1949 article were reviewed. Studies that did not meet the following conditions were excluded from the scope.

Figure 1. Flowchart on data collection.

(1) Being based on research

(2) no theoretical work or conceptual paper

(3) No meta-analysis and meta-synthesis work

(4) No systematic literature review study

(5) Not focusing only on distance education or face-to-face education

(6) Related to higher education

(7) Publication in English

As a result of the scanning, 1406 publications were excluded from the scope of the studies.

At the next stage, the full texts of the studies were reached. Content analysis of the study was carried out, and it was examined in detail whether it complied with the above conditions. As a result of the last review, 225 publications were excluded. There are 318 publications left for content analysis.

Data Analyzes

The articles retrieved as a consequence of scanning through the database were investigated in-depth, and it was determined whether or not they fulfilled the requirements of the research objective. At the level of deep analysis, the first thing that is done is to determine whether or not there is a distinct area for “recommendations for future studies.” It has been pointed out that articles on this topic often include headings like “the limitations of the study,” “Limitations,” “Recommendations” and “Research Implications.” Then we check that there is any recommendation for future researchers. In the following phase, if there is no particular section, other parts of the paper, such as the conclusion and discussion, were analyzed, and recommendations for future researchers were cited. Two authors are responsible for controlling all articles separately and determining excerpt-related sections. Then all teams read independently and coded each excerpt. To assure reliability, the codes were refined until a consensus about their use could be reached. Code reliability was accepted 100 %. Then the codes were merged to form categories and themes. In the findings section, direct quotations were included to support the Codes.

While presenting the study data, statistical information about the publications was first shared, and then the findings obtained from the content analysis were shared.

There was a rise in the number of publications beginning in 2017 and continuing through 2020 ( Figure 2 ). The year 2020 saw the greatest number of publications. Even though there is a reduction in 2021, it is still significantly greater than in previous years. It is possible that the mandatory implementation of blended and remote learning procedures as a result of the pandemic caused the surge that occurred in the years 2020 and 2021.

Figure 2. Number of articles over year.

As shown in Figure 3 , according to total citation, Computer and Education is the first rank. The second rank is “Internet and Higher Education.” Based on the number of articles, the first rank is “Education and Information Technologies” and the second order is “BMC Medical Education” with ten articles. The last rank is “SAGE Open” with four articles and 53 citations. Due to the technology dimension in blended learning, journals related to technology and the internet have naturally come to the fore.

Figure 3. Compare the article’s number and total citation based on journal.

When the papers with the highest citations were analyzed ( Table 1 ), they were connected to the flipped classroom concept, which falls under the umbrella of blended learning. Although it was released later than the other nine studies, the one by Han and Ellis (2019) made it onto the list of the top ten. There are several research approaches and methodologies. Studies that follow participants over time are known as longitudinal studies. Other types of studies include qualitative and experimental research.

Table 1. Top 10 most cited article.

When the studies are analyzed ( Figure 4 ), it is found that 66 of the studies have a distinct part labeled “recommendations” “Future research” or “the limitations of the study” in which recommendations and proposals for more research might be made. In addition, 43 of the papers feature additional parts that contain recommendations for the continued study of blended learning. The word “suggestions” was used as the heading for 23 different articles that offered advice to professional practitioners. 111 of the 251 papers that did not have a distinct title for their suggestions had textual advice for future study. These recommendations were written in the articles. These recommendations were often provided in the form of a distinct paragraph inside the “result” section; however, in certain instances, they were voiced within the discussion sections of the respective articles. There was no future study suggestion on blended learning in any of the remaining 140 of the 251 publications.

Figure 4. Connection separate section and recommendations for future studies.

When we classify according to the fields of the studies examined, 53 studies are composed of non-specific studies. Thirty-one studies are related to the STEM field covering physics, chemistry, mathematics, science, engineering, and environmental education. Considering the 21 studies in the field of health education together, the STEM field has the highest rate with a total of 52 studies. Language education comes next with 31 studies. The other 18 studies were conducted in social science, adult education, sport, and social work.

Content analysis was performed in the “future research proposals” section. These recommendations fall into two main categories ( Figure 5 ). At the first level, they are research content, replication, and method. The codes in the first category are “Other data collection tools,” “Arranging other activities” and “Focusing on components.”

Figure 5. Code and categories.

The authors recommended collecting data from other data sources during the research process. The researcher may have offered such suggestions because they had difficulties collecting in their context or because they needed different data to enrich the process. Other Data Collection Tools code is used for 80 studies.

In the study conducted by Gjestvang et al. (2021) , interviews were done with the participants during the data collection process. Based on this result, they stated that “ Further research on this topic should interview blended learning students at the end of the program ” in the recommendations.

Also, “ Further study should also focus on variables such as the participants’ English level, motivation level, autonomy level, learning style, and gender while measuring students’ perceptions of the blended course ” ( Wang et al., 2021 ) and “ Further studies are planned to monitor the engagement, satisfaction, and learning outcomes of students as the subject evolves over a series of semesters. ” Fisher et al. (2017) quotes were made to more than one data collection tool. The inclusion of such data collection aspects will also differentiate the research process.

The second code is “arranging other activities”: this code includes suggestions for differentiating the activities done in the learning process. It is coded in 19 articles. For example, “ future research can focus on investigating student engagement in learning scenarios aimed at presenting new content rather than being limited to revision lessons ( de Brito Lima et al., 2021 ).” As stated, it is recommended that future studies produce new content.

It is suggested to include other activities according to the course scope in which the blended learning process is applied. These suggestions are mostly seen in studies where language teaching is used. For example, a “ Conducting similar studies that measure the effect of blended learning on some aspects related to English learning such as vocabulary, spelling, and pronunciation ” recommendation was presented based on the results of the study in which blended learning was applied in English teaching by Hijazi and AlNatour (2020) .

The other code is “Focusing on other components.” In this coding, blended learning is used regarding the subject of the applied course and other components related to the concept taught. This code was used in 20 studies. In the survey conducted by Hasanah and Malik (2020) , the “ Future researchers are expected to widen the implementation of the blended learning model not only in the employability aspects related to critical thinking and communication skills but also in other competencies based on the discipline on which they focus. ” proposal was presented. Similarly, based on the result of the study by Mese and Dursun (2019) , “ future studies could conduct with different kind of elements .” was proposed. In addition, in the survey by Nurkhin et al. (2020) , suggestions were made on the use of LMS, which is a component of blended learning. The quote in the study is as follows: “ It is hoped that future researchers will be able to improve the ability of online learning management systems they can better implement blended PBL .”

The replication category contains suggestions to repeat the research under certain conditions. The authors generally support conducting studies that are somewhat similar to the investigation. In this category, “Other disciplines,” “Implementing other courses,” “Diverse sample,” “Other participants” and “Larger sample” stand out. “Deep analyzes” and “Long term effect” branches were evaluated in replications and methodology categories.

The “Other disciplines” code was generally used for studies where blended learning studies were recommended to be applied to other disciplines and was coded six times. For example, as a result of the López-Pellisa et al. (2021) survey in the writing assignment, the authors suggested, “ Future research could be expanded to other academic contexts, within and beyond the humanities, and to other languages .” In the study by Dakduk et al. (2018) , a sample was taken to cover the whole University. The authors recommended more specific studies involving different disciplines. The authors offer their suggestions: “ In future research with executive education, comparing different professional areas and program content (finance, marketing, human resources, and management) should be considered since those variables could modify the relationship to adopt new technologies in executive education .”

The code of “Implementing other courses” is used for suggestions about doing studies that are done in a narrower scope or that are not done within the scope of one course within the scope of the other course. Twenty-seven articles of recommendation in this context were encountered. For example, the study by Ghazal et al. (2018) did not specify a specific course. Based on this result, the authors used the expression “ Based on these limitations, future research designs may consider examining how different types of courses and activities can influence students’ perception of the LMS environment. ” to suggest that the study be carried out within the scope of a specific course. Hinojo-Lucena et al. (2020) , on the other hand, did their work within the scope of the Applied Sciences I course. Based on the results of the study, it then proposes to do it more specifically in the courses in the second year. The authors suggest, “ For future lines of research, it is proposed to analyze this teaching and learning process in the second year of Basic Vocational Training and other modules . “ The study conducted by Bayyat (2020) wanted it to be applied in different theoretical and practical courses. The author used the phrase “ Future research can explore other dimensions in different theoretical and practical courses, cultures, and societies .” for this suggestion.

The “Other participants” code suggests that the authors should collect data from different participants in future studies. This code was used in 8 studies. In the study conducted by Manzanares et al. (2017) , only data were collected from students, and he suggested that teachers be included in future studies. The recommendation was, “ In this study, student-teacher, student-content, and student-system interactions have been analyzed. However, in future investigations, student-student and teacher-system relations will be studied to analyze whether these behavioral patterns influence the results of student learning and can predict the detection of at-risk students .” It was also stated that other data sources would be needed.

Similarly, in the study by Zimba et al. (2021) , collecting data from students and administrators was suggested. The authors stated, “ We recommend that a comparative study be conducted with social work educators in distance-teaching institutions since all participants in this study were from contact teaching institutions. We also recommend more research on BL that includes the voice of the students and university administrators .”

The work meant to be explained with the “diverse sample” code is the enrichment of the group. This code was used for 13 runs. This code includes suggestions such as collecting data across the country and collecting data from different education levels. The study by Xu et al. (2020) included students at a particular university. Based on this result, the authors proposed, “ Further studies of online learning, in more diverse settings and with random assignment of students, will be required to confirm the potential benefits of blended learning .” Similarly, “ Future research could expand the study in diverse educational settings ( Zhu et al., 2021 )” and “ Similar studies could be conducted with different participants at other educational levels to reach a general result and make comparisons ( Talan and Gulsecen, 2019 ),” it has been suggested to work with various samples by applying it at different education levels.

The “Larger sample” code is especially used by researchers working with small groups. They have recommended working with large study groups to generalize the studies. This code was used for 50 runs. The perception of the study group as small also depends on the study methodology. For example, in the study by Zeqiri and Alserhan (2021) , data were collected from 369 people. The authors suggested a “larger sample” based on the study’s results. The authors expressed this: “Finally, a larger and more balanced sample would benefit this study to generalize findings on students’ satisfaction with blended learning .” In the study conducted by Sitthiworachart et al. (2021) on the e-Business Course, there were 25 participants in the sample. Based on the study results, the authors “ further studies need to be conducted to measure the impact of the proposed blended learning activities on a larger sample or with higher-achieving students .” suggested working with a larger group. Again, according to the result of the study conducted by Moradimokhles and Hwang (2020) on 60 nurses, the statement “ Furthermore, the study could be extended to investigate these issues in other students, as the participants of this study were nursing students .” work is recommended.

The “deep analyzes” code was used eight times for studies where the authors suggested deep research should be done. According to the results of the study by Dooley et al. (2018) , the authors interact with the expression, “ Further studies are required to understand better the behavior of students interacting with online resources, and the patterns of behavior associated with engagement and academic performance ” with online resources and the patterns of behavior associated with engagement and academic performance. Again, as a result of the study by Bouilheres et al. (2020) , “ Deeper studies are needed to determine the appropriateness and effectiveness of each activity and/or learning material used in the delivery of every program having implemented a Blended Learning Model. ” is suggested to carry out a detailed study. On the other hand, Taylor et al. (2018) stated that more detailed analyzes are needed to make sense of the concepts. The authors expressed this: “ Further research could investigate more deeply the actual meanings of these terms through focus groups with both faculty and administrators. ”

Studies in which the “long-term effect” code is expressed in the limitations of a study are short-term. To express this awareness, the authors suggest that future researchers measure their long-term effects. This code has been used in 8 publications. For example, in the study by Simko et al. (2019) , the statement “ A future study should consider the long-term outcomes of flipped courses and whether reported initial successes outlast the instructors who first delivered the courses .” is included. In the study by Shimizu et al. (2019) , the expression “ we recommend future research to investigate long-term effects of bPBL ” was used.

The researchers also suggested the “Comparative Studies” code, which was used 3 times in the studies, to conduct comparison studies. Based on the results of the study conducted by Zimba et al. (2021) on social work education, the authors said, “ We recommend that a comparative study be conducted with social work educators in distance-teaching institutions since all participants in this study were from contact teaching institutions .” was suggested. On the other hand, Sanjeev and Natrajan (2019) suggested that a comparison study should be made by differentiating blended learning with the statement, “ There can be a comparative study of different formats of blended learning .”

The “Longitudinal Studies” code was used in seven studies. The researchers considered their studies cross-sectional and suggested longitudinal studies for future studies. Based on the result of the study by Yorganci (2020) , she proposed, “ Besides, longitudinal studies also should be carried out to clarify the effects of FL approach on the learning outcomes in the long term. ” Ghazal et al. (2018) suggested a longitudinal study to visualize the LMS interactions used fully. For this suggestion, the authors used the expression, “ Future studies may also consider conducting a longitudinal study to increase the ability to make causal inferences related to the students’ use of LMS .”

The “Changing methodology” code used in a total of 25 studies is one of the most common codes used by researchers. Researchers suggest that the study be repeated by changing the method or research approach. For example, in the study conducted by Yang and Kuo (2021) , they used a qualitative approach. On the other hand, researchers suggested planning experimental research with the statement, “ For future studies, pre-and post-tests on global literacy are suggested to provide statistical evidence of global literacy improvement .” The study by Engelbertink et al. (2021) used a non-experimental approach based on Interviews and online survey data. The authors were randomized with the statement, “ Further research using a Randomized Controlled Trial among our students will yield more insight into the engagement and motivation of the students using the course, its effectiveness, and the role of PT in this respect ,” proposed a study. Yick et al. (2019) said “ A qualitative research design may provide a detailed understanding about the response and preferences of students on the use of blended learning and their perceived experiences of online learning in the first year of fashion education. A pre-test and post-test design can also help examine differences in improvements in SRL and sewing techniques before and after using online modules .” suggests replicating the study by changing the method.

The number of publications increased beginning in 2017 and continuing through 2020. The number of publications peaked in the year 2020. A result of the bibliometric study covering 2012–2020 by Limaymanta et al. (2021) stated that most publications were made in 2019. This result may be due to the fact that not all publications of 2020 were included, as the study covered the period until November 2020. However, it has been determined that there has been an increase in the number of articles in recent years. In this process, the effect of the pandemic may be. During the pandemic process, many institutions have preferred online, blended learning methods ( Alsarayreh, 2020 ; Andrzej, 2020 ). Researchers have researched blended learning to examine this compulsory condition (e.g., Subandowo et al., 2020 ; Zhu et al., 2021 ).

When the studies are evaluated, it is determined that 66 of the studies have a section labeled “recommendations” “Future research” or “the limitations of the study” in which recommendations and requests for more research may be made. Additionally, 43 publications provide supplementary sections with recommendations for future study. 111 of the 251 articles without a defined title for their proposals had textual recommendations for further research. Only about half of the studies have recommendations for future research. However, this section, which is seen both as a contribution of researchers to the field and as a part of the research process ( Sahu, 2013 ; Mackey and Gass, 2015 ), has not been taken into account.

The suggestions made by the researchers were gathered under the categories of “Research Content” and “Replication and Method.” The maximum number of “Other Data Collection Tools” codes was determined in the “Research Content” category. Researchers consider it important to diversify data sources. The diversity of data in many areas provides convenience in controlling the accuracy of data in research ( Massey et al., 2016 ). Another code is “Arranging other activities.” Blended learning can have rich content, including face-to-face and online content and teaching approaches. According to Medina (2018) , if it is to serve as a support source—a means to an end—that expands the scope of traditional instructional and learning actions while simultaneously fostering independent and lifelong learning skills and practical uses of technology, effective blended learning must become more personalized, flexible, and on-demand. This situation can offer diversity to researchers. “Focusing on other components” is the last code in this category. Because there are different design approaches in blended learning design, research can focus on various components because there are different design approaches ( Alammary et al., 2014 ; Manwaring et al., 2017 ; Thai et al., 2017 ).

For the transfer of work to other domains, there are two codes in the Replication category: “Other disciplines” and “Implementing other courses.” While the first code’s researchers concentrated on various disciplinary applications by evaluating a larger region, the others were more interested in the immediate environment and proposed that it be used to analogous courses. This finding appears to be the researcher’s decision in several ways. For example, Thai et al. (2017) “To confirm the current findings and evaluate additional “blends” in higher education, this study must be replicated with students from different courses and universities” have justified the replication. In the Replication category, the “Larger sample”, “Diverse sample” and “Other participants” branches are related to the sample size. Whether the number of people in the sample is large or small depends on the research methodology ( Chatterjee and Diaconis, 2018 ; Lakens, 2022 ). But researchers care about working with a larger sample. The “larger sample” code was used the most in the codes related to sampling.

“Deep analyzes” and “Long term effect” were the codes we approved in both the replication and method categories. Both codes suggest that additional investigation into the integrated learning process is needed. Due to the variety and enrichment of the instruments employed in the blended learning process ( Engelbertink et al., 2021 ), as well as necessary processes such as the pandemic, long-term research on blended learning will be required ( Dziuban et al., 2018 ; Subandowo et al., 2020 ).

“Comparative Studies” “Cross-Cultural Studies” “Longitudinal Studies” and “Changing methodology” are all sections of the methodology category. In blended learning implementations, there is a wide range of methodologies such as quantitative approach ( Han and Ellis, 2019 ), experimental method ( Hijazi and AlNatour, 2020 ), and qualitative approach ( Taylor et al., 2018 ). With this understanding, the authors believe that their study may be applied to various situations and methodologies.

The number of publications increased starting in 2017 and continuing through 2020. In the year 2020, most articles were published. When the studies are examined, it is discovered that 66 of them have a section labeled “recommendations” “Future research” or “the limitations of the study.” In addition, 43 of the papers have sections with research recommendations. There were textual recommendations for future research in 111 of the 251 publications that did not have a label for their ideas. Approximately half of the publications provide recommendations for future research. The STEM field has the highest rate in selected studies. The researchers’ recommendations were gathered under the titles “Research Content” and “Replication and Method” the researchers’ recommendations were gathered. “Other Data Collection Tools” is the most coded category under “Research Content.” Diversification of data sources is important to researchers. The Replication category has two codes for the transfer of work to other domains: “Other disciplines” and “Implementing other courses.” The Replication category’s “Larger sample” “Diverse sample” and “Other participants” branches all deal with sample size. The study strategy determines whether the sample size is large or small. The “larger sample” code was the most common among the sampling-related codes. In both the replication and procedure categories, we accepted the codes “Deep analyzes” and “Long term effect” The category “Comparative Studies” includes subsections such as “Cross-Cultural Studies, “ “Longitudinal Studies, “ “Changing methodology” and “Methodology.”

Only publications from journals indexed in the Scopus database were included in the study, which is one of the study’s limitations. In the course of the investigation of the recommendations made in the research, content analysis was performed on the statements made by the authors. There has been no investigation into whether or not the intended published research scope is appropriate. The “Recommendations for future research” section might be examined for its level of quality by researchers in the future. It is possible to determine whether the codes produced by investigations of the same kind in several fields are field-independent. In addition, our investigation was limited to papers based on previous research. The breadth of data sources may be enlarged without more limits being added, as the focus of future research will be on theoretical investigations. In addition, it will be of use to researchers in that it will remind them of the significance of “recommendations for future research”.

Author Contributions

RP, NO, and SD contributed to conception and design of the study. SD and SBD searching database and analysis. RP and NK wrote the first draft of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Allen, I. E., Seaman, J., and Garrett, R. (2007). Blending in: The Extent and Promise of Blended Education in the United States . Needham, MA: Sloan-C.

Google Scholar

Alammary, A., Sheard, J., and Carbone, A. (2014). Blended learning in higher education: three different design approaches. Australas. J. Educ. Technol . 30, 643–646. doi: 10.14742/ajet.693

CrossRef Full Text | Google Scholar

Alsarayreh, R. (2020). Using blended learning during COVID-19: the perceptions of school teachers in jordan. Cypriot J. Educ. Sci. 15, 1544–1556.

Andreev, V., Gildenberg, B., Gorbunov, V., Evdokimova, O., and Trofimova, I. (2022). The principles of the implementation of gaming technologies in a blended learning environment in a technical university. Educ. Self Dev. 99, 1–10. doi: 10.26907/esd.17.1.04

Andrzej, O. (2020). Modified blended learning in engineering higher education during the COVID-19 lockdown — building automation courses case study. Educ. Sci. 10:292.

Arrosagaray, M., González-Peiteado, M., Pino-Juste, M., and Rodríguez-López, B. (2019). A comparative study of Spanish adult students’ attitudes to ICT in classroom, blended and distance language learning modes. Comp. Educ. 134, 31–40. doi: 10.1016/j.compedu.2019.01.016

Banyen, W., Viriyavejakul, C., and Ratanaolarn, T. (2016). A blended learning model for learning achievement enhancement of thai undergraduate students. Int. J. Emerg. Technol. Learn. (IJET) 11:48. doi: 10.3991/ijet.v11i04.5325

Baragash, R. S., and Al-Samarraie, H. (2018). Blended learning: investigating the influence of engagement in multiple learning delivery modes on students’ performance. Telematics Inform. 35, 2082–2098. doi: 10.1016/j.tele.2018.07.010

Bayyat, M. (2020). Blended learning: a new approach to teach ballet technique for undergraduate students. Turkish Online J. Distance Educ. 21, 69–86. doi: 10.17718/tojde.727979

Belonovskaya, I., Kiryakova, A., Goriainova, T., and Drobot, M. (2021). Developing the potential of visualization technologies in hybrid tuition. Educ. Self Dev. 16, 127–144. doi: 10.26907/esd.16.3.12

Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, C. A., Tamim, R. M., Surkes, M. A., et al. (2009). A meta-analysis of three types of interaction treatments in distance education. Rev. Educ. Res. 79, 1243–1289. doi: 10.3102/0034654309333844

Bervell, B., Nyagorme, P., and Arkorful, V. (2020). Lms-enabled blended learning use intentions among distance education tutors: examining the mediation role of attitude based on technology-related stimulus-response theoretical framework. Contemporary Educ. Technol. 12, 1–21. doi: 10.30935/cedtech/8317

Bouilheres, F., Le, L. T. V. H., McDonald, S., Nkhoma, C., and Jandug-Montera, L. (2020). Defining student learning experience through blended learning. Educ. Inform. Technol. 25, 3049–3069. doi: 10.1007/s10639-020-10100-y

Bruff, D. O., Fisher, D. H., McEwen, K. E., and Smith, B. E. (2013). Wrapping a MOOC: student perceptions of an experiment in blended learning. MERLOT J. Online Learn. Teach. 9, 187–199.

Cabi, E. (2018). The impact of the flipped classroom model on students ’ academic achievement. Int. Rev. Res. Open Distributed Learn. 19, 202–221.

Chatterjee, S., and Diaconis, P. (2018). The sample size required in importance sampling. Annals Appl. Probability 28, 1099–1135.

Chen, L. L. (2022). Designing online discussion for hyflex learning. Int. J. Educ. Methodol. 8, 191–198. doi: 10.12973/IJEM.8.1.191

Cortez, C. P. (2020). Blended, distance, electronic and virtual-learning for the new normal of mathematics education: a senior high school student’s perception. Eur. J. Interact. Multimedia Educ. 1:e02001.

Dakduk, S., Santalla-Banderali, Z., and van der Woude, D. (2018). Acceptance of blended learning in executive education. SAGE Open 8:21582440188. doi: 10.1177/2158244018800647

de Brito Lima, F., Lautert, S. L., and Gomes, A. S. (2021). Contrasting levels of student engagement in blended and non-blended learning scenarios. Comp. Educ. 172:104241. doi: 10.1016/j.compedu.2021.104241

De Jong, T., Linn, M. C., and Zacharia, Z. C. (2013). Physical and virtual laboratories in science and engineering education. Science 340, 305–308.

Dooley, L. M., Frankland, S., Boller, E., and Tudor, E. (2018). Implementing the flipped classroom in a veterinary pre-clinical science course: student engagement, performance, and satisfaction. J. Vet. Med. Educ. 45, 195–203. doi: 10.3138/jvme.1116-173r

PubMed Abstract | CrossRef Full Text | Google Scholar

Dziuban, C., Graham, C. R., Moskal, P. D., Norberg, A., and Sicilia, N. (2018). Blended learning: the new normal and emerging technologies. Int. J. Educ. Technol. Higher Educ. 15:3. doi: 10.1186/s41239-017-0087-5

ElSayary, A. (2021). Using a reflective practice model to teach STEM education in a blended learning environment. Eurasia J. Mathematics Sci. Technol. Educ. 17:em1942. doi: 10.29333/ejmste/9699

Engelbertink, M. M. J., Kelders, S. M., Woudt-Mittendorff, K. M., and Westerhof, G. J. (2021). Evaluating the value of persuasive technology and the role of teachers in a blended learning course for social work students. Soc. Work Educ. 40, 333–349. doi: 10.1080/02615479.2020.1715935

Fisher, R., Ross, B., LaFerriere, R., and Maritz, A. (2017). Flipped learning, flipped satisfaction, getting the balance right. Teach. Learn. Inquiry 5, 114–127. doi: 10.20343/teachlearninqu.5.2.9

Garrison, D. R., and Kanuka, H. (2004). Blended learning: uncovering its transformative potential in higher education. Internet Higher Educ. 7, 95–105. doi: 10.1016/j.iheduc.2004.02.001

Ghazal, S., Al-Samarraie, H., and Aldowah, H. (2018). “i am Still Learning”: modeling LMS critical success factors for promoting students’. experience and satisfaction in a blended learning environment. IEEE Access 6, 77179–77201. doi: 10.1109/ACCESS.2018.2879677

Gjestvang, B., Høye, S., and Bronken, B. A. (2021). Aspiring for competence in a multifaceted everyday life: a qualitative study of adult students’ experiences of a blended learning master programme in Norway. Int. J. Nursing Sci. 8, 71–78. doi: 10.1016/j.ijnss.2020.11.001

Graham, C. R. (2006). “Blended learning systems: definition, current trends, and future directions,” in The Handbook of Blended Learning: Global Perspectives, Local Designs , eds C. R. Bonk and C. J. Graham (Switzerland: Pfeiffer Publishing).

Graham, C. R. (2013). Emerging practice and research in blended learning. Handb. Distance Educ. 3, 333–350.

Han, F., and Ellis, R. A. (2019). Identifying consistent patterns of quality learning discussions in blended learning. Int. Higher Educ. 40, 12–19. doi: 10.1016/j.iheduc.2018.09.002

Hasanah, H., and Malik, M. N. (2020). Blended learning in improving students’ critical thinking and communication skills at University. Cypriot J. Educ. Sci. 15, 1295–1306. doi: 10.18844/cjes.v15i5.5168

Hijazi, D. A., and AlNatour, A. S. (2020). The effect of using blended learning method on students’ achievement in english and their motivation towards learning it: blended learning, achievement, and motivation. Int. J. Virtual Personal Learn. Environ. 10, 83–96. doi: 10.4018/IJVPLE.2020070106

Hinojo-Lucena, F. J., Trujillo-Torres, J. M., Marín-Marín, J. A., and Rodríguez-Jiménez, C. (2020). B-Learning in basic vocational training students for the development of the module of applied sciences I. Mathematics 8:1102. doi: 10.3390/math8071102

Kintu, M. J., Zhu, C., and Kagambe, E. (2017). Blended learning effectiveness: the relationship between student characteristics, design features and outcomes. Int. J. Educ. Technol. Higher Educ. 14:7. doi: 10.1186/s41239-017-0043-4

Lakens, D. (2022). Sample size justification. Collabra: Psychol. 8:33267.

Lee, J., Lim, C., and Kim, H. (2017). Development of an instructional design model for flipped learning in higher education. Educ. Technol. Res. Dev. 65, 427–453. doi: 10.1007/s11423-016-9502-1

López-Pellisa, T., Rotger, N., and Rodrípguez-Gallego, F. (2021). Collaborative writing at work: peer feedback in a blended learning environment. Educ. Inf. Technol . 26, 1293–1310. doi: 10.1007/s10639-020-10312-2

Lim, C. P., and Wang, L. (2016). Blended Learning for Quality Higher Education: Selected Case Studies on Implementation from Asia-Pacific. Thailand: UNESCO Bangkok Office.

Limaymanta, C. H., Apaza-Tapia, L., Vidal, E., and Gregorio-Chaviano, O. (2021). Flipped classroom in higher education: a bibliometric analysis and proposal of a framework for its implementation. Int. J. Emerg. Technol. Learn. 16, 133–149. doi: 10.3991/ijet.v16i09.21267

Lu, O. H. T., Huang, A. Y. Q., Huang, J. C. H., Lin, A. J. Q., Ogata, H., and Yang, S. J. H. (2018). Applying learning analytics for the early prediction of students’ academic performance in blended learning. Educ. Technol. Soc. 21, 220–232.

Maarop, A. H., and Embi, M. A. (2016). Implementation of blended learning in higher learning institutions: a review of literature. Int. Educ. Stud. 9:41. doi: 10.5539/ies.v9n3p41

Mackey, A., and Gass, S. M. (2015). Second Language Research: Methodology and Design. Milton Park: Routledge.

Manwaring, K. C., Larsen, R., Graham, C. R., Henrie, C. R., and Halverson, L. R. (2017). Investigating student engagement in blended learning settings using experience sampling and structural equation modeling. Int. Higher Educ. 35, 21–33. doi: 10.1016/j.iheduc.2017.06.002

Manzanares, M. C. S., Sánchez, R. M., García Osorio, C. I., and Díez-Pastor, J. F. (2017). How do B-learning and learning patterns influence learning outcomes? Front. Psychol. 8:745. doi: 10.3389/fpsyg.2017.00745

Massey, P. M., Leader, A., Yom-Tov, E., Budenz, A., Fisher, K., and Klassen, A. C. (2016). Applying multiple data collection tools to quantify human papillomavirus vaccine communication on Twitter. J. Med. Int. Res. 18:e6670.

McCutcheon, K., O’Halloran, P., and Lohan, M. (2018). Online learning versus blended learning of clinical supervisee skills with pre-registration nursing students: a randomised controlled trial. Int. J. Nursing Stud. 82, 30–39. doi: 10.1016/j.ijnurstu.2018.02.005

Means, B., Toyama, Y., Murphy, R., and Baki, M. (2013). The effectiveness of online and blended learning: a meta-analysis of the empirical literature. Teachers College Record 115, 1–47. doi: 10.1177/016146811311500307

Medina, L. C. (2018). Blended learning: deficits and prospects in higher education. Australasian J. Educ. Technol. 34, 42–56. doi: 10.14742/ajet.3100

Mese, C., and Dursun, O. O. (2019). Effectiveness of gamification elements in blended learning environments. Turkish Online J. Distance Educ. 20, 119–142. doi: 10.17718/tojde.601914

Moradimokhles, H., and Hwang, G. J. (2020). The effect of online vs. blended learning in developing English language skills by nursing student: an experimental study. Interact. Learn. Environ. 1–10. doi: 10.1080/10494820.2020.1739079

Norberg, A., Dziuban, C. D., and Moskal, P. D. (2011). A time-based blended learning model. Horizon 19, 207–216. doi: 10.1108/10748121111163913

Nurkhin, A., Kardoyo, Pramusinto, H., Setiyani, R., and Widhiastuti, R. (2020). Applying blended problem-based learning to accounting studies in higher education; optimizing the utilization of social media for learning. Int. J. Emerg. Technol. Learn. 15, 22–39. doi: 10.3991/IJET.V15I08.12201

Owston, R., York, D., and Murtha, S. (2013). Student perceptions and achievement in a university blended learning strategic initiative. Int. Higher Educ. 18, 38–46. doi: 10.1016/j.iheduc.2012.12.003

Phillips, J. A., Schumacher, C., and Arif, S. (2016). Time spent, workload, and student and faculty perceptions in a blended learning environment. Am. J. Pharmaceutical Educ. 80:102. doi: 10.5688/ajpe806102

Picciano, A. G., Dziuban, C., and Graham, C. R. (2014). Blended Learning . London: Routledge.

Porter, W. W., Graham, C. R., Spring, K. A., and Welch, K. R. (2014). Blended learning in higher education: institutional adoption and implementation. Comp. Educ. 75, 185–195. doi: 10.1016/j.compedu.2014.02.011

Pruzan, P. (2016). Research Methodology: the Aims, Practices and Ethics of Science. Berlin: Springer.

Pye, G., Holt, D., Salzman, S., Bellucci, E., and Lombardi, L. (2015). Engaging diverse student audiences in contemporary blended learning environments in Australian higher business education: implications for design and practice. Australasian J. Inform. Systems 19, 1–20. doi: 10.3127/ajis.v19i0.1251

Sahu, P. K. (2013). Research Methodology: A Guide for Researchers In Agricultural Science, Social Science and Other Related Fields. Berlin: Springer.

Sanjeev, R., and Natrajan, N. S. (2019). Role of blended learning environment towards student performance in higher education: mediating effect of student engagement. Int. J. Learn. Change 11, 95–110. doi: 10.1504/IJLC.2019.101678

Shimizu, I., Nakazawa, H., Sato, Y., Wolfhagen, I. H. A. P., and Könings, K. D. (2019). Does blended problem-based learning make Asian medical students active learners?: a prospective comparative study. BMC Med. Educ. 19:147. doi: 10.1186/s12909-019-1575-1

Shu, H., and Gu, X. (2018). Determining the differences between online and face-to-face student–group interactions in a blended learning course. Int. Higher Educ. 39, 13–21. doi: 10.1016/j.iheduc.2018.05.003

Simko, T., Pinar, I., Pearson, A., Huang, J., Mutch, G., Patwary, A. S., et al. (2019). Flipped learning–a case study of enhanced student success. Australasian J. Eng. Educ. 24, 35–47. doi: 10.1080/22054952.2019.1617650

Sitthiworachart, J., Joy, M., and Mason, J. (2021). Blended learning activities in an e-business course. Educ. Sci. 11, 1–16. doi: 10.3390/educsci11120763

Smolyaninova, O., Gruzdeva, E., and Smolyaninov, A. (2021). Online mediation in the socialization of children with disabilities: environmental conditions in the Arctic and the North of the Krasnoyarsk Territory. Educ. Self Dev. 16, 346–361. doi: 10.26907/esd.16.3.28

Subandowo, M., Asri Humaira, M., Rusmiati Aliyyah, R., Rachmadtullah, R., Samsudin, A., and Nurtanto, M. (2020). Use of blended learning with moodle: study effectiveness in elementary school teacher education students during the COVID-19 pandemic. Int. J. Adv. Sci. Technol. 29, 3272–3277.

Suleri, J. I., and Suleri, A. J. (2018). Comparing virtual learning, classical classroom learning and blended learning. Eur. J. Sustainable Dev. Res. 3:em0072. doi: 10.20897/ejosdr/3970

Talan, T., and Gulsecen, S. (2019). The effect of a flipped classroom on students’ achievements, academic engagement and satisfaction levels. Turkish Online J. Distance Educ. 20:3. doi: 10.17718/tojde.640503

Taylor, M., Ghani, S., Atas, S., and Fairbrother, M. (2018). A pathway towards implementation of blended learning in a medium sized Canadian university. Int. J. Online Pedagogy Course Design 8, 60–76. doi: 10.4018/IJOPCD.2018010105

Thai, N. T. T., De Wever, B., and Valcke, M. (2017). The impact of a flipped classroom design on learning performance in higher education: looking for the best “blend” of lectures and guiding questions with feedback. Comp. Educ. 107, 113–126. doi: 10.1016/j.compedu.2017.01.003

Vasyura, S., Kuzmina, O., and Maletova, M. (2020). Internet communications: time phenomenon and communicative activity. Educ. Self Dev. 15, 71–79. doi: 10.26907/esd15.4.03

Wang, N., Chen, J., Tai, M., and Zhang, J. (2021). Blended learning for Chinese university EFL learners: learning environment and learner perceptions. Comp. Assisted Lang. Learn. 34, 297–323. doi: 10.1080/09588221.2019.1607881

Wang, Y., Han, X., and Yang, J. (2015). Revisiting the blended learning literature: using a complex adaptive systems framework. Educ. Technol. Soc. 18, 380–393.

Why choose Scopus - Scopus benefits — Elsevier solutions (2018). https://www.elsevier.com/solutions/scopus/why-choose-scopus (accessed March 3,2022).

Xu, D., Glick, D., Rodriguez, F., Cung, B., Li, Q., and Warschauer, M. (2020). Does blended instruction enhance English language learning in developing countries? evidence from Mexico. Br. J. Educ. Technol. 51, 211–227. doi: 10.1111/bjet.12797

Yang, Y. F., and Kuo, N. C. (2021). Blended learning to foster EFL college students’ global literacy. Comp. Assisted Lang. Learn. 1–22. doi: 10.1080/09588221.2021.1900874

Yick, K. L., Yip, J., Au, S. C., Lai, Y. Y., and Yu, A. (2019). Effectiveness of blended learning in the first year of fashion education. Int. J. Fashion Design Technol. Educ. 12, 178–188. doi: 10.1080/17543266.2018.1546910

Yorganci, S. (2020). Implementing flipped learning approach based on ‘first principles of instruction’ in mathematics courses. J. Comp. Assisted Learn. 36, 763–779. doi: 10.1111/jcal.12448

Zeqiri, J., and Alserhan, B. A. (2021). University student satisfaction with blended learning: a cross-national study between North Macedonia and Jordan. Int. J. Technol. Enhanced Learn. 13:325. doi: 10.1504/ijtel.2021.10036683

Zhu, M., Berri, S., and Zhang, K. (2021). Effective instructional strategies and technology use in blended learning: a case study. Educ. Inform. Technol. 26, 6143–6161. doi: 10.1007/s10639-021-10544-w

Zimba, Z. F., Khosa, P., and Pillay, R. (2021). Using blended learning in South African social work education to facilitate student engagement. Soc. Work Educ. 40, 263–278. doi: 10.1080/02615479.2020.1746261

Keywords : blended Learning, flipped classroom, recommendations for future studies, replication, methodology, research content

Citation: Platonova RI, Orekhovskaya NA, Dautova SB, Martynenko EV, Kryukova NI and Demir S (2022) Blended Learning in Higher Education: Diversifying Models and Practical Recommendations for Researchers. Front. Educ. 7:957199. doi: 10.3389/feduc.2022.957199

Received: 30 May 2022; Accepted: 21 June 2022; Published: 05 July 2022.

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Copyright © 2022 Platonova, Orekhovskaya, Dautova, Martynenko, Kryukova and Demir. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Servet Demir, [email protected]

† ORCID: Raisa I. Platonova, orcid.org/0000-0002-7402-4051 ; Natalia A. Orekhovskaya, orcid.org/0000-0001-8390-5275 ; Saule B. Dautova, orcid.org/0000-0002-5451-4950 ; Elena V. Martynenko, orcid.org/0000-0002-3089-9892 ; Nina I. Kryukova, orcid.org/0000-0002-0667-9945 ; Servet Demir, orcid.org/0000-0003-1360-2871

Theoretical Foundations for Blended Learning

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The practice of blended learning needs to be guided by blended learning theories and other related theories. The emergence and development of blended learning is the product of learning psychology and pedagogy in the information age.

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What Do We Mean by Blended Learning?

Stefan Hrastinski

Blended Learning: The View Is Different from Student, Teacher, or Institution Perspective

Finding the Right Blend: Bringing Learning Back to Blended Learning

The practice of blended learning needs to be guided by blended learning theories and other related theories. The emergence and development of blended learning is the product of learning psychology and pedagogy in the information age. Therefore, the implementation of blended learning needs to be guided by systems theories, educational communication theories, learning theories, teaching theories, and curriculum theories. Certain learning modes, as well as related design and practice models, have emerged from blended learning research and practice. In this chapter, Sect.  1.1 briefly reviews the connotation and development of blended learning; Sect.  1.2 introduces the theoretical basis for the emergence and development of blended learning, pointing out that the increasing prevalence of blended learning is based on the development of teaching and learning in the information age; Sect.  1.3 explains the theoretical basis of blended learning and its guiding role for blended learning; Sect.  1.4 introduces several modes of blended learning and describes how these modes can seamlessly connect online and offline learning; Sect.  1.5 introduces the design models and implementation models of blended learning. As a systematic summary of relevant theories and research on blended learning, this chapter shows the connotations and characteristics of the theories and highlights their guiding significance and value for blended learning. This chapter can be used as a reference by teachers, instructional designers, educational technology personnel, educational administrators, and researchers.

1.1 The Concept of Blended Learning

Blended learning was first introduced in the field of corporate human resources training, aiming to overcome the limitations of time and space in face-to-face teaching, including small class size, poor timeliness, and high training cost. Since the 1960s, some large international companies, such as IBM and Boeing, have attempted to make it possible to train hundreds or even thousands of employees at the same time with the support of communication technology. Communication technology has been developing rapidly, evolving from the original mainframe computers and minicomputers to television media in the 1970s, CD-ROMs in the 1980s, and to various communication methods based on the Internet in this century. No matter how the technology has changed, the purpose of blended learning in corporations remains the same, that is to overcome the human resource constraints and maximize training effectiveness (Bersin 2004 ). The face-to-face learning component plays a vital role in training work skills and the inheritance of corporate culture. Therefore, integration of the technology-based learning mode and the advantages of the face-to-face mode has been adopted by corporate human resources training.

In the 1990s, Internet-based E-Learning had gradually become popular with the development of information technology. Due to the differences in the media, the approaches, and the target audience’s needs between face-to-face learning and E-Learning, the two learning methods, to a large extent, were separated during that period. The E-Learning mode provided learners with a richer technical environment and a more convenient way to obtain resources. However, the E-Learning mode also had some disadvantages, such as low engagement and a poor real-time interactive experience. It is recognized that students have difficulties in completing learning tasks independently in the unsupervised network environment. As a result, a more effective and flexible blended learning method has been applied in teaching and learning by education researchers and practitioners. “Blended learning” has been proposed as a proper term. Initially, blended learning was considered as a simple combination of E-Learning and face-to-face learning, as moving classroom teaching to the Internet via information technology, or as supplementary extra-curricular learning tasks. Perceptions about blended learning have gradually changed; now it is seen as a learning mode that can improve classroom learning. An increasing number of researchers have begun to realize that the word “blended” should be considered as “integration” and “fusion”, instead of simply referring to “combination”. Blended learning is not limited to merely integrating face-to-face and online learning environments, but is a systematic reconstruction of multiple elements including learning resources, teaching strategies, learning environments, learning tools, and teaching and learning models.

From a historical perspective, as a social activity, education is certainly affected by social productivity, particularly technological advancement. Communication technology is the most important technology that affects education. Its development has revolutionized education. In primitive society, education was combined with life and labor without the distinction between formal and informal. Body language, as the dominant means of communication, aimed to maintain livelihoods. In an agricultural society, the dominant communication means used in education were word of mouth and hand-compiled books. Due to different needs in the society, formal and informal education was distinguished. Formal education was mainly in government and private schools, where teaching was conducted in either a centralized or a decentralized manner. Moreover, personalized learning was adopted without distinction among classes and school years. Informal education referred to the development of labor skills by using scenario-based learning for agriculture, and apprenticeship, etc. In industrialized society, word-of-mouth as the means of communication was adopted in education, though the bulk-printing of books and basic computer technology were also included. Due to the need for a large amount of standardized manpower, formal education shifted from the elite to the public, with classroom teaching as the main teaching mode. As a result, standardization and large-scale education came into being, such as schools, school years, curriculum, and courses. At the same time, the focus of informal education changed from labor skills in agricultural society to work skills, while the teaching strategy changed from scenario-based learning for agriculture to factory apprenticeship.

In the information society, information and communication technology are the foundation of the society; information resources are the major development resources; and digital industries are the leading field in the society. Information, together with matter and energy, constitutes the three key indispensable resources. Multimedia technology and Internet technology are widely integrated in education. Formal education has changed from popularization in industrial society to universalization in the digital society. In addition, with the learner-centered perspective, formal education has maintained its scalability and added the personalization. The teaching strategy has changed from face-to-face classroom teaching in the industrial society to the integration of scheduled face-to-face learning and flexible technology-enabled learning, such as a hybrid form that merges physical space and virtual cyberspace. With the development of emerging information technologies, such as cloud computing, Internet of Things, artificial intelligence, and biological computer technology, physical space and virtual cyberspace will be integrated more deeply. As a result, education has the potential to satisfy the learning need for everyone and can happen anytime and anywhere, allowing the seamless integration between formal and informal education, supporting personalized and lifelong learning. In this way, learning can move towards a new ecology of ubiquitous learning.

1.2 The Rationale for the Emergence and Development of Blended Learning

Blended learning is a learning mode that integrates face-to-face learning and technology-enabled learning. In order to achieve optimal learning effect under specific conditions, blended learning reconstructs the core elements of education, including goals, content (resources), media, methods, evaluation, and teaching teams, based on the nature of education, the laws of education and learning, and required future manpower. As a product of societal, economic, and technological development, it is certain that blended learning will become the new norm of education. Blended learning can not only meet the needs of societal development for education, but also meet the requirements of individual development. The emergence and development of blended learning has a solid theoretical foundation in psychology and pedagogy.

1.2.1 Psychological Rationale

Blended learning attends to both the commonality and individuality of students

Psychological research points out that people have common and individual traits, some highly relevant to teaching, while some not very relevant. Within the common and individual traits related to student learning, those that are not related to the content of a particular discipline are commonly referred to as common traits and individual traits of student learning in psychology.

The fact that students have learning-related common traits provides foundations for face-to-face teaching and online synchronous teaching, while students’ learning-related individual traits demonstrates the role that E-Learning can play. Therefore, the emergence and development of blended learning is historically inevitable. In the following part, the common features, and individual characteristics of student learning in a general sense will be briefly described so as to gain a clearer understanding of how to design good blended learning accordingly.

Characteristics of common traits

The psychological development of an individual is sequential and phased, therefore students in different age ranges will show corresponding and common general characteristics, including stage characteristics of cognitive development, psychosocial development, and moral development.

Concerning the characteristics of cognitive development , the well-known psychologist Jean Piaget proposed the four Piagetian stages of cognitive development, namely Sensorimotor Stage (0–2 years old), Preoperational Stage (2–7 years old), Concrete Operations Stage (7–11 years old), and Formal Operations Stage (11 years old and later). Identifying learner characteristics of cognitive development at each stage is the key foundation to instructional design.

Regarding the characteristics of psycho-social development , Erik H. Erikson developed his eight stages of personality development that integrate self-development and environmental influences (Erikson  1964 ). Trust and Mistrust (birth to 18 months): at this stage, children feel secure if they receive love and affection in a stable and predictable environment. This security allows them to trust others, otherwise babies will mistrust. Autonomy and Doubt (18 months to 3 years old): at this stage, children who are allowed freedom to explore, within limits, learn self-confidence, otherwise they may become discouraged and begin to feel worthless. Initiative and Guilt (3 to 6/7 years old): at this stage, children are bundles of energy, full of imagination and initiative. They begin to master peer relationships and language. If they are not encouraged to participate, they may feel guilty about the extent of their own ambitions and fail to develop the skills to play and work with others. Industry and Inferiority (6/7 to 12 years old): children at this stage begin to undertake some tasks independently and work together with others. If teachers can encourage and praise them, it is more likely that children will develop a sense of diligence and a proactive personality, otherwise, they are prone to develop a sense of inferiority. Identity and Identity Confusion (12 to 18 years old): children in this period start to develop self-identity. In other words, individuals try to establish a coherent sense of self (including his/her physical appearance, previous situations, status-quo, the limitations of environment and conditions, and the prospect of his/her future) as a whole. If children are provided with the right guidance from teachers and parents, they will successfully construct their self-identity, otherwise confusion of self-identity may appear. Intimacy and Isolation (18 to 30 years old): this period is the stage of love, marriage, and early family life. Youths seek to develop intimate personal relationships with others without losing their self-identity. If they fail to do that, they will develop a sense of loneliness. Generativity and Stagnation (30 to 60 years old): this is the challenge of the middle years of life. Raising children, creative activities, and community service are ways people give to others in this stage. Being unable to contribute in these ways can bring about boredom, restlessness, stagnation, and a feeling that life is meaningless. Integrity and Despair (after age 60): being able to look back on life with contentment and few regrets is the main task of Stage 8. Integrity involves having a good perspective on life in one’s final years. People who struggled through life without feeling a part of it may end up facing death in despair.

According to Erickson, there are corresponding key influencers in each of the above stages, namely: mother, father, family members, neighbors, as well as school teachers and students, peers and small group members, friends, colleagues, spouses, and the whole human race (Erikson  1964 ). Characteristics of psycho-social development influence the establishment of personality. How people interact with others and things affects the development of their personalities.

Regarding the characteristics of moral development , Lawrence Kohlberg proposed three moral levels and six stages of moral reasoning. The three levels are pre-conventional morality, conventional morality, and post-conventional morality. Each level has two stages. These stages can serve as reference for the development of students’ morality (Shaffer and Kipp 2012 ).

The characteristics of cognitive development affect the design of the difficulty level of learning objectives, the abstraction of the content, the format of learning resources, and the design of learning activities. The characteristics of psycho-social development may constrain the design of emotional interaction between teacher-student, student–student, teams, activities, as well as feedback and assessment; while the characteristics of moral development affect the design of learning activity guidelines.

In a society that promotes lifelong learning, learners can range from primary to middle school students, college students to the elderly. All learners have the above-mentioned characteristics of cognitive, psycho-social, and moral development related to learning as common features. For learners of the same age, such common features can serve as the basic guidelines for designing learning objectives, content, resources, activities, collaboration, interaction, feedback, and assessment in face-to-face or real-time online learning. Yet, for the learners of different age groups, self-paced online learning might work better. Therefore, it is necessary to develop blended learning.

Individual traits

Human development involves not only common features, but also individual characteristics. In other words, people have individual differences which are influenced by genetics, social living conditions, education, and other factors in their process of socialization. Some are related to learning, including differentiated traits like learning interests and learning styles.

Learning interest demonstrates learners’ willingness to learn. Psychologists divide learning interests into personal interests and situational interests. Personal interests are idiosyncratic and relatively stable, referring to a person’s tendency to pay attention to specific stimuli, objects, and topics. Situational interests, on the other hand, are responsive. When situational interests are “triggered”, they can attract learners’ attention in a short period of time. If situational interests are “maintained”, they can promote students to stay focused on the same task or topic over a long period of time (Ormrod 1999 ).

Generally, interests can facilitate information processing more efficiently (Ormrod 1999 ). In addition, to some extent, interests and learning mutually reinforce each other. When students experience a sense of competence, their learning interest may increase. Even if students are not initially interested in some learning content or an activity, they may develop an interest after experiencing success. Therefore, it is necessary to understand interests of learners, to trigger and maintain learners’ personal and situational interests, to have a variety of teaching modes, and an autonomous learning atmosphere. All of these key elements can be included in blended learning.

Learning style refers to the psychological characteristics indicating learners’ perception of stimuli and their responses to the stimuli. In other words, learners tend to choose special strategies in their learning process. The following section mainly discusses learners’ different needs for learning environments and their different cognitive styles.

Learners’ different needs for learning environments

Affective needs refer to learners’ need for encouragement and comfort in their learning process. Social needs refer to learners’ need for peer discussion. Environmental and emotional needs refer to learners’ preference towards environment and emotions when learning, such as studying in a quiet environment, having snacks when reading, walking back and forth when thinking, or having a certain efficient learning period.

Differences in cognitive styles

Cognitive style refers to the strategies learners are used to adopting when they perceive, recall, and reflect. It shows the individual differences of learners in the process of organizing and processing information and reflects the different characteristics of learners in perception, memory, reflection, and problem-solving abilities. Each learner can have a variety of cognitive styles at the same time and utilize different combinations of them in the process of learning. Mainly, four types of cognitive styles have an impact on instructional design: the preferred sensory channel for perceiving or receiving stimuli, field-independent and field-dependent, holistic and sequential, and reflective and impulsive.

The preferred sensory channel for perceiving or receiving stimuli refers to the sensory channels that learners prefer in learning, including visual, auditory, and tactile/ kinesthetic.

Field-dependent and field-independent . The concept of the field dependence–independence cognitive style emerged as a result of the work of Witkin. A relatively field-independent person is likely to overcome the organization of the field, or to restructure it, when presented with a field having a dominant organization, whereas the relatively field-dependent person tends to adhere to the organization of the field as given (Witkin et al. 1977 ). Witkin et al. ( 1977 ) claimed that field independent individuals rely on an internal frame of reference, while field dependent individuals rely on an external frame of reference. Whilst field dependent individuals have a preference to learn in groups and to interact frequently with one another as well as the teacher, field independent learners may respond better to more independent and more individualized approaches. Also, field independent learners are more likely to have self-defined goals and to respond to intrinsic reinforcement, whilst field dependent learners require more extrinsic reinforcement and more structured work by the teacher. Whereas the field independent learners prefer to structure their own learning and to develop their own learning strategies, field dependent learners may need more assistance in problem-solving strategies or more exact definitions of performance outcomes (Witkin et al. 1977 ). Field independent individuals are more capable of dealing with situations requiring impersonal analysis, whilst field dependent individuals are better equipped to deal with situations requiring social perceptiveness and interpersonal skills.

Holistic and sequential . When dealing with learning tasks, individuals have two tendencies: one is a holistic, hypothesis-oriented strategy, which deals with tasks as a whole and tests relatively more complex hypotheses at the same time; the other is a sequential, fact-oriented, step-by-step strategy, which tests only one limited hypothesis at a time. Holistic learners are good at solving problems from a comprehensive and holistic perspective. They prefer to grasp the overall situation, and then find a breakthrough to solve problems, or solve complex problems first. They have high intuition and ambiguity, but low accuracy and profundity. In contrast, sequential learners use the “operational” method to learn. They are used to dividing problems into details to understand them and solving problems step by step, according to a logical sequence. They are also good at discovering the differences between different entities.

Reflective and impulsive . The concept was originally introduced by Kagan et al. ( 1964 ) to describe the individual differences in the speed with which decisions are made under conditions of uncertainty to employ impulsive or reflective cognitive tempos.

Impulsive children respond quickly with short latencies and numerous errors, while reflective children tend to inhibit their initial responses and to reflect upon the correctness of their responses, thereby exhibiting longer latencies and fewer errors.

Reflective children tend to analyze stimuli and organize them into detail components and, accordingly, perform better on tasks requiring attention to details. Impulsive children, on the other hand, tend to focus more on the stimulus as a whole and thus perform better on tasks requiring a more global analysis.

Since different learning activities require different psychological characteristics, it can only be said that a certain tendency is more suitable for a certain learning context, rather than that a learner with a certain tendency is necessarily smarter than one with another tendency.

In a relatively flexible and autonomous learning mode, blended learning can provide students with more choices in terms of the learning environment and learning partners, giving feedback via the system, resources via various media (such as visual, audio and text media), and allowing students to follow at their own pace. Thus, blended learning can meet the needs of students with different learning styles.

Blended learning provides personalized learning paths or pacing for students with different potential. The previous subsection discussed the common features and individual characteristics related to learning in general. To account for these characteristics simultaneously, a flexible learning mode like blended learning is needed and will become more prevalent. In fact, in psychology, some individual characteristics of learners, such as the disciplinary learning potential, learning needs, and learning competence of different students, are closely related to subject learning, but are difficult to be taken into consideration by face-to-face teaching alone; thus they would benefit from online teaching.

Multiple intelligence structure

Howard Gardner, a professor of psychology at Harvard University, proposed the Multiple Intelligences Theory after years of research. He defined intelligence as a “biopsychological potential to process information that can be activated in a cultural setting to solve problems or create products that are of value in a culture”. On this basis, he proposed nine different types of intelligence, namely, linguistic intelligence, logical/mathematical intelligence, spatial intelligence, bodily-kinesthetic intelligence, musical intelligence, interpersonal intelligence, naturalist intelligence, intrapersonal intelligence, and existential intelligence. Everyone was born with more than eight types of intelligence that are both independent and interrelated and has different strengths and weaknesses in intelligence. When solving problems and creating products, people combine and use these intelligences differently, which gives rise to each person’s different and individualized multiple intelligence structure. For students, each subject may tap multiple intelligences and involve their various combinations, which explains why they are talented and full of potential in one subject but lacking in potential in another subject.

Gardner’s “Multiple Intelligences Theory” helps educators to be aware of the multiple differences among students, and explains why some students can learn subjects they are good at easily and fast, but relatively hard and slowly when they learn the subjects that they are not good at. Online learning has the advantage to make full use of “multiple intelligences” to teach, to enable students to have their individualized learning paths, and to help students learn at their own pace (Zhang 2002 ).

Learning needs

Learning needs refer to the gap between what the learner wants to get out of the learning experience and their current state of learning and development. Due to learners’ differences in terms of their living environments, future jobs and positions, and their development potential, differences can be found in learners’ learning expectations.

Meanwhile, since the current learning levels of learners are also different, their learning needs vary too. Due to the large number of people in traditional classrooms, teaching is at the same pace and teachers cannot take into account the learning needs of different students. However, blended learning, which adopts a learning mode that combines “online and offline”, can expand the time and space of learning and thus meet the learning needs of different students. With a variety of learning resources, students can not only review and relearn, but also learn more content more deeply.

Learning competence

Learning competence refers to people’s ability to acquire knowledge, work on tasks, and seek development (Liu et al.  2018 ). Learning competence includes general abilities and specific abilities (Gao 1989 ; Zeng and Cao 2005 ). General ability is a comprehensive ability that is applicable to all or most studies. Although it is not discipline-specific, it has an impact on discipline learning and has the characteristics of transferability, universality, wide application, and stability. Yin and Bi ( 2000 ) categorized general learning abilities into basic abilities and comprehensive abilities, and proposed that the basic abilities of learners include observation ability, memory ability, thinking ability, and expression ability, while comprehensive abilities include self-learning ability, problem-solving ability, experimental ability, and creativity. It is also proposed that learners’ basic abilities and comprehensive abilities are cultivated through the learning of professional knowledge of a discipline, and can be applied to new learning, serving not only as the basis, but also the purpose of learning.

Special abilities are the abilities demonstrated in professional activities, such as disciplinary abilities. Lin ( 1997 ) believes that the intelligence and ability of learners should be organically combined with general abilities of the discipline, such as listening, speaking, reading, and writing in language subjects. The combination of discipline-related intelligence and competencies, strategies, and methods should also be included. Gardner’s ( 1993 ) multiple intelligence structure links disciplines to the intellectual structure of learners. He stated that everyone has a different match from the eight or nine types of intelligence, which explains why people perform differently in different subject areas.

There are differences in general learning abilities and the disciplinary competence of leaners, which directly affect the way, the efficiency, and the quality of their completion of disciplinary learning activities. Blended learning can not only provide a variety of learning paths and various learning support methods, but also enable learners to review, repeat, or learn more things, helping them to exercise and develop their own learning ability. Therefore, blended learning can resolve learners’ differences in learning ability and facilitate learners to better complete their learning tasks.

1.2.2 Pedagogical Rationale

Blended learning takes into account common features and individual characteristics of learners and enables learners with potential in different disciplines to learn through different learning paths at their own pace. Meanwhile, the information society requires education to promote the holistic and personalized development among students, which serves as an important pedagogical basis for the emergence and development of blended learning.

Blended learning realizes the essence of education—to promote “the development of each student” . Education is a social activity in which educators should have a positive impact on students. Having an accurate perspective on students is very important to systematically develop education. Likewise, such a perspective is crucial to realize the essence of education—“to promote the development of each student and improve the life quality and value of each one”. According to Gardner’s theory of multiple intelligences, although students’ intellectual structures vary, they all have unique potentials, which means that there are no students that cannot learn, only different students. At the same time, there are differences among students in terms of learning foundation, learning speed, learning interest, learning motivation, learning needs, and learning ability. Therefore, if schools can provide multiple development approaches for each student, it is possible to further develop each student’s superior intelligence, thus encouraging their learning motivation and facilitating the development of their weaker intelligence area to a certain extent. In this way, each student can enjoy a successful learning experience at school and contribute to the achievement of the educational purpose, which is to promote the development of every student.

Promoting the development of each student requires schools to carry out customized and individualized teaching and learning. Teaching and learning needs to be tailored to the unique needs of students. However, the realization of real personalized teaching and learning is a huge challenge for schools. It is not feasible to equip each student with a tutor, yet the development of technology enables the possibility to promote the personalized development of students. Learning analysis based on big data, adaptive systems, and Massive Open Online Courses (MOOC), has created conditions for providing students with suitable learning content and learning methods. “In the age of technology, people are more likely to pursue learning on their own and will not feel the sense of failure that comes when everyone is supposed to learn the same thing at the same time” (Collins and Halverson 2009 , p. 110). Moreover, the development of information technology facilitates a variety of learning methods, from E-Learning to U-learning, which enable students to have more space to learn outside the classroom.

In recent years, scholars have examined how technology can facilitate personalized teaching and learning. For instance, Wei et al. ( 2019 ) identified seven behaviors of students in the classroom by using intelligent learning analysis technology, namely, listening to class, looking around, raising hands, sleeping, standing, reading, and writing. This technology can offer timely and accurate feedback on the learning of each student in the classroom, which can help teachers enhance teaching strategies, and optimize classroom learning and management. This will improve the efficiency of teaching and learning and contribute to the reform of personalized teaching and learning.

Since 2016, Tsinghua University has launched a smart teaching and learning tool—Rain Classroom. The tool covers every data collection session, from “before class” to “during class” to after class”. The back-end of the tool records detailed data of teaching and learning behavior, such as the number of students participating in the classroom, the timeslot that students enter the classroom, the slides they fail to understand, the questions that are answered incorrectly, the frequency that a preview video is watched before the class, and the completion and correct rate of the after-class homework. Such data can clearly restore most of the teaching and learning process in the real classroom. Using such data for data analysis and mining can support teachers to enhance the teaching process and help students to enhance the learning process. By adopting machine learning and artificial intelligence, the panoramic recording of big data will provide the foundation for teachers and students to make decisions in a scientific fashion, including individually analyzing past teaching and learning processes, objectively reflecting on the current teaching and learning situation, and proactively arranging future teaching and learning (Wang 2017 ).

Blended learning is one of the important approaches for schools to achieve student-centered personalized teaching and learning. On the one hand, blended learning allows teachers to implement various student-centered offline learning modes so that they can have interaction and communication with students. On the other hand, with the advantages of promoting personalized development among students, blended learning can take advantage of emerging technologies to break the limitations of time and space for providing students with personalized learning. By integrating online and offline learning, blended learning aims to “deliver ‘appropriate’ skills to ‘appropriate’ learners at ‘appropriate’ times by applying ‘appropriate’ learning techniques that fit ‘appropriate’ learning styles” (Singh and Reed 2001 ). In this way, learners will be able to have a personalized learning experience rather than learning in a one-size-fits-all classroom (Horn and Staker   2017 ).

Therefore, blended learning meets the essential requirements of education, follows the fundamental principles of education, and will become increasingly more common in education.

Blended learning helps to cultivate talents with twenty-first century core competences . So far, human beings have witnessed hunter-gatherer society, agricultural society, industrial society, and the move towards an information society (Toffler 1990 ). In the agricultural society, education was through apprenticeship or one-to-one tutoring, generally with only one room as the school building. In the industrial society, in order to meet the needs of large-scale teaching and learning, modern schools emerged and the education system transformed into “Factory Models of Schools” (Duan et al. 2009 ). In the twenty-first century, with the fast knowledge update and the diverse ways of knowledge acquisition, traditional teaching and learning are unable to adapt to the increasingly complex living and working environments. This is because society has put forward higher requirements for talents in terms of creativity, diversity, and individualization.

In the era of rapid change, the education field has been changing in order to cope with the development of the new era. Countries and international educational organizations have a common challenge to understand what kinds of talents to be trained for the new century. According to the Organization for Economic Cooperation and Development (OECD), the talents to be cultivated should have the following characteristics. The first is reflection, a relatively complex mental process including metacognition, creativity, and critical thinking. The second is the ability to cope with complex problems and unpredictable scenarios. According to the EU, the expectation is to cultivate talents with competences including critical thinking, problem-solving, teamwork, communication and negotiation skills, creativity, cross-cultural communication, and life-long learning (European Commission 2018 ). Regarding the development of core competences in Chinese students, it is expected that students will have six core competences, namely, humanity, scientific spirit, ability to learn, healthy life, responsibility, and innovation (Research Group on Core Literacy 2016 ).

By comparing the eight frameworks for core competences in the world, Dutch scholar Voogt and others came to the following conclusions: ① Four core competences are advocated by all the frameworks, namely collaboration, communication, ICT-related competences, and social and/or cultural awareness (including citizenship); ② The other four core competences advocated by most frameworks are creativity, critical thinking, problem-solving, and the capacity to develop high-quality products or productivity. These eight competences are the common pursuit of human beings in the information age and are called “the world common core competences” (Voogt and Roblin 2012 ). The above-mentioned competences can be further refined into the following four, namely, collaboration, communication, creativity, and critical thinking, which are the “twenty-first century 4Cs”. The world common core competences are the common pursuits of human development goals in the information age, which reflect educational trends in the world.

Blended learning has the advantages of offline, face-to-face classroom teaching and the advantages of online learning, such as various learning models, self-paced learning, idea sharing, resource sharing, and collaborative, inquiry-based problem solving. Blended learning can promote the development of students’ autonomous learning ability, identification ability, critical thinking skills, and creativity, which are the talents needed in an information society.

Researchers advocate that blended learning can contribute to the cultivation of twenty-first century core competences in students. Zhang et al. ( 2019 ) used the Wisdom Tree platform to establish a blended learning model based on the Small Private Online Course (SPOC), and found that students’ autonomous learning ability and learning efficiency were enhanced by adopting this model. Wang et al. ( 2018 ) conducted a survey on college students who participated in blended learning courses based on Massive Open Online Courses (MOOC). Their study found that this model improved students’ language expression, autonomy, and teamwork, strengthened the teacher-student and student–student communication, and thus improved learning effectiveness. These studies suggest that blended learning plays an important role in enhancing students’ problem-solving skills, teamwork, and other higher-order thinking skills.

Apparently, blended learning can help to cultivate talents with twenty-first century skills. To achieve this goal, blended learning needs to utilize the teacher-led and student-centered learning model to promote students’ autonomy and provide authentic problems to cultivate students’ problem solving skills. Also, blended learning needs to integrate the advantages of various teaching modes to provide students with appropriate learning paths and focus on collaborative learning to cultivate their collaboration and communication skills. Moreover, students should be offered a variety of learning tools and sufficient technical resources for self-paced learning so that students can become lifelong learners. The cultivation of students’ critical thinking and innovation skills should be integrated in the teaching and learning process. Nowadays, with rich online resources, students should be exposed to different ideas and be offered with more opportunities for hands-on practice and expression of their opinions with proper guidance.

All colleges and universities in China now consider blended learning as a development direction of educational reform. Colleges have started to use the established online resources, such as Chinese University MOOC, Chaoxing, and Zhihui Shu (Wisdom Tree) to support blended learning. More importantly, colleges need to choose or establish their own learning management systems, develop online resources, and implement multi-modal blended learning, so that blended learning can be widely adopted in all fields and disciplines of higher education, thus contributing to the reform of teaching and learning. The sixth part of this handbook provides helpful examples of blended learning in colleges and universities.

According to the theoretical foundations in psychology and pedagogy, blended learning should have face-to-face and online learning closely coordinated and seamlessly connected, considering the common features and the individual characteristics of students. Additionally, blended learning should respect the common development principles of learners at different stages and meet their individual learning needs. Therefore, blended learning requires a diversified design of learning activities and a hierarchical design of learning resources to allow different learning paces and paths, as well as personalized guidance for learners, according to their learning effectiveness, so that their learning potential can be fully developed. Meanwhile, blended learning can adopt more teaching strategies, such as independent learning, collaboration, and inquiry, to cultivate students’ core competences in the information age.

1.3 The Theoretical Basis of Blended Learning

To sum up, blended learning conforms to the laws of psychology, the nature of education, and its future trends. Blended learning is beneficial to cultivating individualized talents required by social development. The next question is how to design ideal blended learning. This section will illustrate the theoretical basis for identifying the ideal instructional design of blended learning, including systems theory, educational communication theory, learning and teaching theory, and curriculum theory.

1.3.1 Systems Theory and the Guidance for Blended Learning

Systems thinking was introduced by the Austrian American theoretical biologist, L. Von. Bertalanffy, in 1932. By using the concept of system as the focus, systems thinking explores the basic framework and methods that can tackle the complexity and dynamics of a system in a more appropriate and effective way. It emphasizes considering issues as a system. In other words, the components and the interrelationships and interactions among these components and their interactions with the environment should be considered, and the system should be processed as a whole to generate an overall effect of “1 + 1 > 2”.

The basic viewpoints of systems thinking are as follows: problems should be addressed as a whole; the emphasis is on the interconnection and interaction among systems, components and environments; the structure of the system is the internal basis for the system to maintain integrity and certain functionality; systems are hierarchical—a system itself is also a component part of a larger system, while a component is a subsystem made up of components of a lower layer; systems are dynamic, purposeful, and open; and exchange with the environment on matter, energy and information (Dou 1988 ).

Systems thinking is an important guiding ideology of blended learning. Blended learning considers online and offline learning as a whole. In other words, it is a process of design, development, implementation, evaluation, feedback, and refinement of online and offline learning. This process needs good planning and seamless connection between online and offline learning. Meanwhile, a good instructional design of blended learning requires systems thinking to involve the participation of several parties (such as content experts, technicians, graphic designers, instructional design experts, teachers, and students); the variety of teaching and learning modes and process (such as learning objectives, learning content, the composition of learners and their characteristics, online and offline learning activities, learning space, other teaching strategies, assessment and evaluation, teachers, learning support staff, the teaching and learning environment, and community). Moreover, the implementation of blended learning needs to consider learning support, online and offline learning activities, communication with peers and communities, the tracking of the learning process, and according personalized adjustment based on data analytics. In addition, the evaluation of blended learning needs to integrate online learning data and offline classroom performance to consider the relationship between students’ overall development and personality development. Several factors need to be balanced when focusing on learning outcomes and the learning process.

Systems Approach and the implications for blended learning

The scientific systems approach, or a systems approach, is a method according to the systematisms of things. It examines the research object as a whole with organization, structure, and function (Li et al. 2007 ). To be specific, it comprehensively examines research objects from the aspects of mutual relationship between the system and the components, between components and components, and between the system and the external environment. The general process of applying a systems approach to solve problems includes five basic steps: define the problem and set the goal, identify solutions, select the strategies, implement the solution, and evaluate the effectiveness. This problem-solving approach focuses on situational analysis and a holistic view, as well as relationship analysis. It is the general approach to support the whole process and the sub-processes of design, implementation, evaluation, feedback, and improvement in blended learning. Further, the application of the systems approach in blended learning can ensure optimal learning effectiveness.

The Theory of Complex Systems and the implications for blended learning

The Theory of Complex Systems is a series of theories about complex systems and is the further development of the Theory of General Systems. The Theory of General Systems refers to human-made established systems with central control mechanisms, stable structures, and predictable changes, while the Theory of Complex Systems focuses on complex systems without central control mechanisms and stable structures. Such systems are not designed, but evolve from the interactions between many dynamic components at the group level. The evolving process is a bottom-up process, as a self-organized process from messy to organized at the group level with numerous unpredictable possibilities. Along with the bottom-up process, the system will develop a top-down regulation process. Thus there is tension between the bottom-up process and the top-down process of the system. When this tension is directed toward adaptation to the environment, it not only generates many possible changes, but also filters the possibilities according to adaptability requirement, thus guiding the system to evolve and finally develop a system structure and operation mechanism that is adaptive to the environment.

In fact, teaching and learning is a complex system in itself. Blended learning is more complex than pure face-to-face or online learning. The goal of blended learning is to integrate the advantages of various teaching modes to achieve optimal learning effectiveness. How to integrate various teaching modes to develop an optimal teaching and learning system is particularly important because such teaching and learning systems include various elements, complex relationships, and unpredictable evolution processes. Students and teachers who pursue personalized teaching and learning play an active role. The required complex teaching system can be self-formed through the self-adaptation of the bottom-up generative process to the top-down environmental requirement and can result in the integration of various teaching modes. For blended learning, it is essential to provide teachers and students with freedom to choose teaching and learning modes independently so as to encourage their enthusiasm, but also to regulate the teaching and learning environment according to expected learning outcomes. By maintaining a reasonable tension, it is possible to integrate a variety of teaching modes and develop an optimal teaching and learning system, thus achieving optimal learning effectiveness.

1.3.2 Educational Communication Theory and Its Implications for Blended Learning

Educational communication is a communication activity between educators and learners in which educators select appropriate content according to the learning objectives and transmit knowledge, skills, ideas, and concepts to specific learners through effective media channels. The essence is to make communication and interaction more effective. Therefore, the theoretical framework of educational communication, which appeared before educational technology, can be used to interpret blended learning, as they both pursue the optimal effectiveness of teaching and learning interaction. With the development of psychology and the increasing recognition of constructivist epistemology, it is gradually realized that educational communication is a multi-directional interactive activity in which ideas and meanings are constructed by learners as subjects instead of being transmitted, thus adding new meaning to educational communication theories.

Communication channel selection and blended learning

Educational communication is a system mainly consisting of four components: educators, educational information, educational media, and learners. The four components interact and develop the following six relationships: educators and learners, educators and educational information, educators and educational media, learners and educational information, learners and educational media, and educational information and educational media (Nan and Li 2005 ). Dealing well with these six relationships is very important to ensure and improve the effectiveness of educational communication.

When educators and learners are identified in the system, the educational information will be provided accordingly. Among all the components, educational media is the most active and abundant, because most forms of media can store and transmit plenty of information, though the effectiveness of different communication channels may vary. This leads to the issue of the choice of communication channels, or even the simultaneous use of multiple channels. On the one hand, some specific content needs to use the most suitable transmission channel to achieve better communication effectiveness. On the other hand, according to constructivism and connectivism (see the Learning Theory section), there are meanings distributed across the connections among different nodes. For learners, they can adopt different educational media to gain information, support their scaffolding, and develop their own cognitive framework and understanding.

There are a variety of communication channels in blended learning that can meet different needs, including different subject matter, students’ learning styles, and students’ meaning construction, and thus enhance communication effectiveness. The relationship of educational media channels with educators, learners, and educational information respectively needs to be well aligned in order to achieve high-quality educational communication effectiveness.

Educational communication model and blended learning

The educational communication model is a theoretically simplified version that replicates the reality of educational communication. It briefly outlines the phenomenon of educational communication and the composition and relationship of various components in the process of educational communication. This model reveals the essential characteristics of educational communication.

The basic model of educational communication

The basic model of educational communication is developed according to the four components of the educational communication system and the important influences of feedback and environment on communication effectiveness. These four components are educators, educational information, educational media, and learners. This model reveals that the effectiveness of educational communication is the result of the interaction and interconnection of all components involved in the communication process instead of being determined by one component. Therefore, the various components in the communication process and the relationships among them should be considered in a holistic manner.

The typical model of educational communication

Based on the basic model of educational communication, whether educators and learners are physically together determines the development of a communication model of face-to-face education or distance education (Wei and Zhong 1992 ).

With the development of technology, the communication model of distance education based on the Internet has gradually become the mainstream. Information suppliers, i.e., information sources, are more diverse. It is more convenient to identify the characteristics of information recipients through big data. According to those relatively accurate data, information or content will be encoded more appropriately.

Means of transmission will be more abundant. Information recipients also have the right to choose. Given various decoding methods, and the different decoding of groups and individuals, interaction, communication and feedback can happen in a timely and multi-directional manner, making the meaning constructed by information recipients more diverse. Accordingly, the communication model of distance education based on the Internet is becoming increasingly diversified.

Blended learning involves face-to-face communication modes and distance, particularly Internet-based communication modes. Its communication processes, such as design, implementation, evaluation, feedback, and improvement, as well as functional elements, should not only follow the steps of these communication models and involve all elements, but also conform to all the principles of communication.

1.3.3 Learning Theories and Their Guidance on Blended Learning

Since the emergence of psychology in 1879, learning theories have gradually developed and resulted in many representative genres. Although the genres have distinctive viewpoints, all of them mainly explore three fundamental aspects: the essence of learning, the learning process, and the principles and conditions of learning (Chen and Liu 2019 ). An in-depth understanding of these theories and the application will help us fully understand the definition, nature, functions, and conditions of learning, thus providing a suitable theoretical basis for different blended learning scenarios.

Behaviorism and its applicable teaching scenarios

In the first half of the twentieth century, behaviorism became dominant in psychology. The core idea of behaviorism is that learning is the result of connections between stimuli and responses through repetitive attempts (Watson and Kimble 2017 ). If the desired response is reinforced in time, it is easier to develop stimulus–response connections and result in learning. The connections between stimuli and responses are emphasized by not only the classical conditioning theory by Ivan Pavlov and John Broadus Watson and the connectionism learning theory by Edward Thorndike, but also the operant conditioning theory by Burrhus Frederic Skinner. Later, some behaviorists began to absorb the ideas of the cognitive school and Albert Bandura’s social learning theory is an example. He believed that learning in essence refers to the process in which individuals obtain some new behavioral responses or amend existing behavioral responses by observing the behaviors of others and their reinforcement results.

Regarding the teaching content of each discipline, there is always something that does not require much thinking but needs to be remembered by students (such as English vocabulary, mathematical symbols, etc.), or something that has been approved by thinking but requires skilled memory and fast responses, or some motor skills. This is the teaching scenario where behaviorist learning theory can come into play. Behaviorism has been playing an important role in all teaching processes, including blended learning.

Cognitivism and its educational applications

In the 1960s and 1970s, behaviorist learning theories showed more and more limitations and could not explain how people learn through internal psychological mechanisms. More and more psychologists began to adopt a cognitivist approach, focusing on learners’ internal processing of knowledge. Based on behaviorist theories of stimulus–response connections, cognitivism emphasizes that the stimulus–response connections are resulted from the formation of cognitive structures, and claims that learning, in essence, is the process in which learners actively form cognitive structures in their minds through understanding. Cognitive learning theories mainly include Jerome Seymour Bruner’s discovery theory, David Ausubel’s assimilation theory, and Robert Mills Gagné’s information processing theory.

The philosophical basis of the cognitivist learning theory is objective epistemology, which recognizes the existence of objective truth or absolute knowledge. The theory determines that teaching, with students’ cognitive structure as the basis, is to persuade students to accept the newly taught knowledge and to incorporate it into their original knowledge structure, which then either expands or changes into a new one. This is how learning occurs.

Blended learning can take advantage of dual or multiple channels to gain a better understanding of students’ cognitive structures. It does so by adopting advanced learning analytics technology, such as big data or data mining, and analyzing the online learning trajectory of learners and the dialogue texts of activities, together with scales and questionnaires. This serves as the basis for the design of blended learning. At the same time, cognitivism emphasizes that learning is a meaningful process in which organisms actively form new cognitive structures. Therefore, the instructional plans of blended learning should be designed to meet the needs of different students so as to arouse students’ learning enthusiasm and enable them to internalize external objective stimuli into their internal cognitive structures through autonomous learning and teacher guidance. Cognitivism emphasizes the need to adopt flexible teaching procedures and instructional modes according to students’ age and experience as well as the nature of the discipline. Thus, in blended learning, appropriate arrangements should be based on learners’ mental development level and cognitive representation, so that they can make connections between their prior knowledge and experience and their new knowledge.

Learning perspective from constructivism and its applications to teaching

Constructivism is a further development of cognitivism and its essence is in direct contrast to the philosophical epistemology of objectivism. Specifically, extreme constructivist epistemology does not recognize the existence of objective truth, while moderate constructivist epistemology believes that the objective truth can be put aside first and the cognition of things is formed through the interaction of an individual’s prior experience with existing things. Different from the view of behaviorism and cognitivism, which regards learning as the individual activity of the learner, constructivism regards learning as a process in which an individual builds knowledge on prior understanding through interactions with social environments.

The pioneer of modern constructivism is Jean Piaget, who believed that knowledge neither comes from the subject nor the object but is constructed in the process of interaction between the subject and the object. The enrichment process of the cognitive structure of the organism is the process of the progressive construction of the cognitive structure of the subject from equilibrium to imbalance and back to equilibrium (Piaget 1997 ). Piaget’s view of cognitive learning is mainly to explain how to internalize the objective knowledge structure into a learner’s cognitive structure through their interaction with it (Piaget 1976 ). Therefore, his constructivist view belongs to cognitive constructivism.

As one of the representatives of social constructivism, Vygotsky emphasized the role of socio-cultural history in psychological development, especially the prominent position of activities and social interactions in the development of people’s higher psychological functions. He believed that higher psychological functions come from the internalization of external actions, which is achieved not only through teaching, but also through daily life, games, and labor (Vygotsky 1980 ). In addition, the inner intellectual action is also externalized into the external actual action, so that the subjective can be seen in the objective. The bridge between internalization and externalization is human activity.

Constructivists emphasize the dynamic nature of knowledge, the richness and diversity of learners’ empirical world, active construction, social interaction, and the situational nature of learning. That learners are builders of their own knowledge is an important theoretical basis for blended learning. It is convenient to use various mediated tools in blended learning. Thus teaching can be placed in a certain context, which can stimulate students’ learning interest and allow them to actively construct meaning (Hung 2001 ). Blended learning can not only promote in-depth, face-to-face communication of all parties through offline teaching, but also promote broader and longer in-depth discussions through the establishment of online virtual communities, in which participants can have multilateral collaboration and even cross-cultural communication to achieve corresponding learning outcomes (Lam 2015 ). Blended learning is more likely to provide a way for students to learn independently, pay attention to the construction of an autonomous learning environment, and then enhance the dominant status of students (Gharacheh et al. 2016 ). Due to the seamless connection between online and offline teaching, more activities can be carried out from class to out-of-class.

The guiding role of humanism in teaching and learning

Humanistic psychology was first proposed in the 1960s and prevailed in the 1970s. It is grounded in the belief that people are innately good and will grow and develop if provided with suitable conditions. Humanism advocates respect for human values and self-actualization and proposes that education should meet the actual needs for learners’ development of human nature. The typical representatives of humanism are Maslow and Rogers.

Abraham Harold Maslow (1908–1970) proposed the hierarchy of needs theory, based on which he further proposed the motivational theory of student learning and development.

The hierarchy of needs theory believes that people’s needs are diverse, and these needs can be divided into 7 levels arranged in a ladder according to their nature (Maslow 1970 ).

The hierarchy of needs has the following relationships: first, after the needs of the lower layer are satisfied, the needs of the higher layer will appear and dominate the individual’s behavior. Second, all needs can be divided into two categories: basic needs and growth needs. Basic needs include the first four levels of needs, all of which are directly related to human instincts, and the satisfaction of which is beneficial to one’s physical and mental health. The last three levels of needs are growth needs, which are driven by the development of one’s self-potential, and the satisfaction of which will bring the greatest degree of happiness to the individual.

Human needs determine their motivation, which means that the nature of needs affects the nature of motivation and the intensity of needs affects the intensity of motivation. Among all the needs, self-actualization is the central idea of Maslow’s motivation theory. Self-actualization means that all organisms are born with special potential, which is also an internal need of the organism. The desire to satisfy such a need drives the organism to realize its full potential.

Carl Ransom Rogers (1902–1987) pioneered “client-centered therapy”, believing that each individual has the potential for healthy growth. As long as a friendly, supportive, and sincere atmosphere is created for patients, the patient will recover on their own with no need for treatment.

During psychotherapy, Rogers developed the theory of personality, in which the notion of self or self-concept becomes the central focus. Self-concept believes that people are innately motivated by “self-enhancement”, which is manifested as the individual’s tendency to maximize their potential. This is the most basic motive and purpose of man and is the same as the Maslow motivation theory.

In addition, Rogers unequivocally proposed to cultivate a “whole person”. It is believed that traditional education only emphasizes cognition, but abandons any emotion associated with learning activities and denies the most important part of itself, which leads to the separation of knowledge and emotion in education. He believed that the ideal education is to cultivate a “whole person” who is “integrated in body, mind, feelings, spirit, and intellect” (Rogers, 1982 ). “Educated people only refer to those who have grasped how to learn and how to adapt to changes, and realized that no knowledge, only the process of seeking knowledge, is reliable.” To achieve this teaching objective, the autonomous learning of learners and the sincere attitudes that teachers show to learners are indispensable. Rogers believed that the key to teaching is not lesson plans, teaching skills, teaching resources, or teaching methods, but the relationship between teachers and students. To this end, teachers should fully trust students to develop their potential, respect learners’ feelings and personal experiences, treat learners with their “true” self, and have empathy. With leaners at the center of teaching, both schools and teachers should work for this learner-centered education (Rogers et al. 2012 ).

Rogers believed that learning is “meaningful” and “self-initiated” and he valued ​the relationship between learning materials and learners’ real life (such as learning interests, expectations, and needs). It is proposed that teachers should be facilitators and adopt a “nondirective” approach. Teachers and learners must gain mutual trust and follow eight principles: first, teachers and students jointly formulate curriculum plans and management methods and share responsibilities. Secondly, teachers provide students with various learning materials, including their own learning experience, books, reference materials, etc. Thirdly, learners’ exploration of their own interests should be taken as an important teaching resource and learners should be required to make a learning plan independently or with peers. Fourth, a good atmosphere for learning should be established. Fifth, the focus of learning is not on the content, but on the continuity of the learning process; the goal of teaching is not to have students master “what they need to know”, but to know how to master “what they need to know”. Sixth, learning objectives should be set by learners themselves and the realization of this goal should be promoted through the “self-training” of learners. Seventh, the learning results should be evaluated by students. Eighth, learners should be encouraged to display immersive emotions and reasoning in the learning process from the beginning to the end, so that learning can become an integral part of their lives and behaviors.

Both Maslow and Rogers pointed out that education, instead of being received from outside, should be self-initiated by students. Schools and teachers should create a good educational environment and a friendly, supportive, and sincere atmosphere for students. In this way they can have their basic needs satisfied and in turn spontaneously pursue self-actualization out of growth needs, fully tapping into their potential and achieving their value.

As a powerful supplement to behaviorism and cognitivism, the philosophical thoughts and learning theory of humanism have important guiding significance for teaching, especially in blended learning.

Connectivism and blended learning

Connectivism learning theory was proposed by George Simmons and Stephen Downes in 2005 (Siemens 2005 ; Downes 2005 ). The theory was born at a time when human society, with the challenges brought by rapid changes in and emergence of knowledge, tended to be digitalized, networked, and intelligent. It is an important theory to explain how learning occurs in the network age. This theory believes that knowledge is constantly changing and is a network phenomenon (Downes 2012 ); learning is not only a process in which connections are established and networks are formed, but also one that promotes the formation and connection of internal cognitive neural networks, conceptual networks, and social networks (Siemens 2005 ). In order to maintain the continuous flow and growth of knowledge, it is necessary to continuously establish, maintain and update connections in complex environments. This theory provides a new perspective for interpreting the learning mechanism in the Internet age and developing instructional designs in cyberspace more effectively.

Siemens proposed eight principles in his paper “Connectivism: A Learning Theory for the Digital Age” (Siemens 2005 ), which was further supplemented several years later, constituting of the 13 basic principles of connectivism.

Learning and knowledge rests in diversity of opinions.

Learning is a process of connecting specialized nodes or information sources.

Learning may reside in non-human appliances.

Learning is more critical than knowing.

Maintaining and nurturing connections is needed to facilitate continuous learning.

Perceiving connections between fields, ideas, and concepts is a core skill.

Currency (accurate, up-to-date knowledge) is the intent of learning activities.

Decision-making is itself a learning process.

The integration of cognition and emotions in meaning-making is important.

Learning has an end goal—namely the increased ability to “do something”.

Organizational and personal learning are integrated tasks.

Learning happens in many different ways. Courses are not the primary conduit for learning.

Learning is a knowledge creation process, not only knowledge consumption.

The connectivism learning theory provides new teaching ideas that adapt to complex network environments and new knowledge concepts, thus it can best reflect the normality of social learning in the current and future network environments (Duke et al. 2013 ). It is conducive to not only promoting innovations in complex and rapidly changing fields, but also training and developing learners’ learning ability and literacy in the digital age, providing them with a broader perspective to grasp the ever-changing growth of knowledge and to adapt to the development of society (Cabrero and Román 2018 ).

Connectivism learning involves both the consumption and production of content. The contribution of learners can expand in the network, such as reflection, critical comments, linked resources, the creation and communication of other digital knowledge, and problem solving (Anderson and Dron 2011 ). The continuous expansion, maintenance, and development of learning networks are the key to maintaining the timeliness and effectiveness of learning among individuals and groups. This point of view also affects the focus of the instructional design in blended learning under the guidance of connectivism, which has shifted to building an ecological environment that is conductive to network development and knowledge growth (Carreño 2014 ).

1.3.4 Curriculum Theory and Blended Learning

Curriculum is related and subject to educational goals and objectives, serving as the concrete embodiment of objectives and as the basis for achieving educational goals. In the meantime, it also constrains teaching and learning modes and strategies. As a theory and method system for curriculum design, the curriculum theory is established on different cognition and value orientations of the disciplines, individual psychological characteristics, and social needs. After the middle of the twentieth century, different curriculum theory genres emerged, such as the knowledge-centered theory, learner-centered theory, and social-centered curriculum theory, all of which are the theoretical basis for the curriculum design of blended learning.

Knowledge-Centered curriculum theory

The development of knowledge-centered curriculum theory involved Herbert Spencer’s substantive curriculum theory and Johann Friedrich Herbart’s intellectualism theory in the nineteenth century, plus the essentialism proposed by Johann Friedrich Herbart and Michael John Demiashkevich, Robert Maynard Hutchins’ perennialism, and Jerome Bruner Seymour Bruner’s subject curriculum theory in the twentieth century.

Intellectualism mainly refers to the genre represented by Johann Friedrich Herbart (Strozier 2002 ). It emphasizes knowledge and its associated intellectual and rational values and advocates knowledge transmission and intelligence development as the basis and purpose of education and teaching and learning processes. It is also emphasized that imparting knowledge is also education, edification, and training, and is the basic approach to provide moral, aesthetic, and religious education.

The main representative of substantive curriculum theory is Herbert Spencer. He defined the purpose and task of education as teaching individuals how to live. Since only science can prepare people for a full life, the knowledge of science is of most value, and thus curricula should be composed of practical scientific knowledge.

The main representatives of essentialist curriculum theory at the early stage include William Chandler Bagley, Michael John Demiashkevich, and F. Alden Shaw. This genre believes that the ultimate purpose of education is to promote social progress and improve the level of democracy. The key factors that determine whether society will advance and develop are personal morality and wisdom, which can be found in excellent cultural heritage from history. Therefore, course content should include common and unchanged cultural elements in cultural heritage, which are the fundamental core of social knowledge.

The main representatives of perennialism curriculum theory include Hutchins of the United States, Alain of France, and L Stone of the United Kingdom (Otiende and Sifuna 1994 ). This genre believes that the nature and purpose of education and curriculum content are eternal; traditional “eternal disciplines” that involve intellectual training are more valuable than practical disciplines. Those “eternal disciplines” with the most valuable knowledge are the most appropriate for schools to include.

Subject curriculum theory, developed in another wave of American education reform led by the noted American psychologist Jerome Seymour Bruner, focuses on satisfying the needs of developing the intellectual resources of modern human beings. The basic ideas of this reform are included in Bruner’s book, The Process of Education , which expounds the four key ideas of this curriculum reform. First, to learn any discipline is mainly to master its basic structure and to master the basic attitude or method of learning the discipline; second, the fundamentals of any discipline can be taught in some form to students of any age; third, teaching in the past only paid attention to the development of learners’ analytical thinking, but in the future, attention should be paid to the development of learners’ intuitive thinking; fourth, the best motivation for learning is to be interested in the learning resources themselves, instead of overemphasizing external stimuli such as rewards and competitions. Among these four key ideas, the core is the basic structure of disciplines.

Knowledge-centered curriculum theory is based on discipline knowledge to explain the curriculum. All knowledge-centered curriculum theories focus on which knowledge is most valuable.

Learner-Centered curriculum theory

The learner-centered curriculum theories originated in the twentieth century, and now mainly include the humanistic curriculum theory represented by Abraham H. Maslow and Carl Ransom Rogers, the empirical curriculum theory represented by John Dewey, and the existential curriculum theory represented by William Morris. With students as the focus, the learner-centered curriculum theories believe that curriculum content should change as students change during the teaching and learning process.

The founders of humanistic curriculum theory are Maslow and Rogers. The theory advocates considering people as a whole, rather than dismembering people’s psychology into several parts that cannot be integrated. It is believed that the fundamental value of education is to help people realize their potential and meet their needs. The purpose of education is to cultivate “a whole person” with good personalities, harmonious development, and freedom. Such a “whole person” can fulfil their potential. In other words, their needs at all levels are met. Also, the harmonious unity of emotional development and cognitive development is achieved. These should be unveiled throughout the entire process of curriculum development, implementation, and evaluation. Humanistic curriculum theory overemphasizes the importance of the “individual” and the values of individualism, which can be considered as its limitations.

Dewey is the representative of the empirical curriculum theory. In the past, curriculum theory witnessed a long-term battle between subject-centered and child-centered instruction. At the beginning of the twentieth century, Dewey resolved this conflict with his unique concept of experience and established the unique empirical curriculum based on naturalistic empiricism (Dewey 1963 ). Dewey ( 2001 ) shared four key ideas about the empirical curriculum theory in his book, Democracy and Education . First, education is continuous reconstruction or reorganization of experience, which adds to the meaning of experience, and which increases ability to direct the course of subsequent experience (p. 81–85). Second, “education as growth” (p. 46). Growth is a natural process, in which people’s habits, minds, and capacities continually grow and improve. Third, “Education as a Necessity of Life”(p. 5). Education itself is the process of life instead of preparation for future life.” Fourth, “Education as a Social Function” (p. 14). If school education is organized according to the form of social life, as a tool and a means to promote social progress and realize the democratic ideal, schools become a form of social organization and social life.

Existential curriculum theory is an educational trend based on existentialism. Existential educational philosophy emphasizes that the main purpose of education is to serve individuals. Education should guide people to become aware of their own environmental conditions, teach individuals to live spontaneously and authentically, and facilitate their smooth engagement in their meaningful existence. The whole emphasis of curriculum for schools must shift from the things to personality. It is advocated that the ideal curriculum should recognize individual differences in experience and take the interests of learners as the basis for learning plans and activities. It advocates activity-based learning starting from learners’ needs. It emphasizes that students have freedom in group and individual activities and recommends the use of a “dialogue” style for individualized learning. Also, it emphasizes reflection and enlightenment in the learning process, against subject-centered learning.

Social-Centered curriculum theory

Social-centered curriculum theory is also known as “social reconstructionist curriculum theory” and its main representatives are George Counts, Harold Rugg, Theodore Brameld, and Paulo Freire. This curriculum theory believes that the fundamental value of education is for social development, emphasizing social problems, and social transformation. It is believed that schools should focus on social transformation rather than personal development. The purpose of education is to “transform society”, according to the subjective blueprint. Schools are the main tools for forming a “new social order”. To this end, school curricula should be organized around the “central issue” of social transformation. The value of the curriculum is social value. The curriculum is the means for realizing the future ideal society. The four key ideas of this theory are as follows: first, the goal of the curriculum is to transform society. Second, the curriculum is centered on a wide range of social issues, which is decided by educators, according to the needs of the society. Third, the curriculum should be organized based on solving practical social problems, rather than subject knowledge. Fourth, in terms of learning strategies, students should be involved in social life as much as possible to enhance their adaptability to social life.

Curriculum theory and blended learning

Knowledge-centered curriculum theory, learner-centered curriculum theory, and society-centered curriculum theory all have different emphases, each of which is important to the current society. Therefore, the design, implementation, and evaluation of the curriculum of blended learning should integrate the above three theories. It is advocated that a curriculum of blended learning should be based on learners’ own needs, interests, development, and self-realization and pay attention to social problems and social needs. Through systematic learning and practice of disciplinary or interdisciplinary knowledge, such a curriculum should be able to meet the needs of social development.

From the perspective of the relationship between the system and the environment, blended learning takes social needs and problems as the input of the environment to the system so that the learning objectives and content can meet the social requirement to students. Blended learning has reshaped the existing form and organization of knowledge, broken down the exclusive nature of disciplinary knowledge in traditional teaching, and thus blurred the boundaries among disciplines. In blended learning, knowledge is identified and selected according to the learning objectives. The learning and application of knowledge are strengthened so that the ability of learners can be improved to meet the needs of social development. Blended learning provides content, resources, and learning paths that suit students’ characteristics to promote students’ self-realization, which represents the concept of learner-centered curriculum. In short, many curricular practices of blended learning are based on project-based learning or problem-based learning. The content of blended learning has focused more on solving specific problems, such as social problems. In the online environment, the learner-centered approach enables learners to set up personalized learning goals, have the autonomy to work on their learning progress, and select the curriculum according to their own needs. In this way, learners can enhance their capabilities of knowledge building and autonomous problem-solving skills.

1.3.5 Instructional Theory and Blended Learning

The design, implementation, and evaluation of curriculum and instruction of blended learning should follow the foundational principles of learning and teaching. In the following section, several major pedagogical theories for guiding blended learning will be introduced.

The theory of Mastery Learning and its implications for blended learning

The theory of mastery learning was founded by Benjamin Bloom and his colleagues. The so-called “mastery of learning” refers to supplementing classroom teaching and learning with frequent and timely “feedback-correction” sessions, in which students can have sufficient study time and receive individual help, so that they can master a unit and continue on a more advanced one, and thereby meet the standards set by the curriculum objectives. The theory of mastery learning contains two implications: first, it is an optimistic theory of teaching and learning; second, it is a set of effective individualized teaching and learning practices that can help most students learn. The core idea is to allow each student to have enough study time. As long as each student is provided enough study time that suits them, they should be able to achieve their learning objectives.

Bloom further pointed out that there are three variables that affect academic achievement: firstly, cognitive entry behaviors include learner’s aptitude and cognitive structure level. They account for 50% of learning. Secondly, affective entry characteristics refer to the integration of non-intellectual factors, such as the learner’s affection, attitude, interest, or confidence. They account for 25% of learning. Thirdly, the quality of instruction refers to whether the presentation, explanation, and arrangement of each element of learning tasks are suitable for learners. It accounts for 25% of learning.

Blended learning can facilitate the realization of mastery learning theory. First, for blended learning, it is more convenient to understand learners’ cognitive level in advance and use online learning resources to enable students to acquire knowledge required at an early stage. Moreover, designing blended learning activities in which students are interested and encouraging students through various ways can facilitate learners’ emotional engagement. Additionally, the online learning space can be fully utilized to provide personalized help and allow an individualized pace, thereby ensuring every learner to achieve their learning objectives.

First Principles of Instruction and their implications for blended learning

Professor M. David Merrill of Utah State University in the United States studied the common basic principles underlying instructional design theories and models. He summarized five principles (Merrill 2002 ). First, problem-centered: Learners learn more when they acquire concepts and principles in the context of real-world tasks. Second, activation: learners learn more when they activate relevant previous knowledge. Third, learners learn more when they observe a demonstration of the skills to be learned. Fourth, application: learners learn more when they apply their newly acquired knowledge and skills. Fifth, integration: learners learn more when they reflect, discuss and integrate their new skills in their everyday life.

Merrill believed that if the First Principles of Instruction can be applied, the effectiveness of teaching strategies will gradually improve. If the principle of “demonstration” is applied, effectiveness will reach the first level; if the principle of “application” is implemented, effectiveness will reach the second level; if the principle of “problem-centered” is implemented, the effectiveness level will reach the third level; if both of the principles of “activation” and “integration” are applied, the level of teaching effectiveness will be improved to a higher level.

The First Principles of Instruction can be used directly to guide the instructional design of blended learning and its implementation.

Scaffolding instruction

“Scaffolding”, originally referring to temporary platforms used to assist construction, is used as a metaphor for the conceptual framework that assists students in problem solving and meaning construction.

The theoretical basis of “scaffolding instruction” originated from the social constructivism founded by the noted psychologist Lev Vygotsky of the former Soviet Union. There are two related basic viewpoints. One, the sociocultural theory, holds that higher mental functions are social by their origin. Higher mental functions, including judgment, reasoning, imagination, intentional recall, will, higher emotion, and language, initially exist as an activity content or form among people. They are internalized by students, as students’ mental ability or inner mental process. Therefore, learning activities serve as important approaches for students to promote the development of their higher mental functions in the context of social interaction with teachers and peers. Second is the Zone of Proximal Development (ZPD) theory, which is “the distance between the current level of development and the level of their potential development” (Vygotsky 1980 ). The former refers to the ability to solve problems independently, while the latter refers the ability to solve problems under the guidance of an adult or in collaboration with more capable peers.

Instruction makes ZPD a reality and creates a new ZPD. The distance between the two levels is dynamic. Everyone’s ZPD is different. In terms of instruction, it is necessary for teachers to fully consider learners’ current level of development, set higher developmental requirements for students according to their proximal development zones, and provide personalized scaffolding.

The instructional design of blended learning can refer to the design of scaffolding instruction theory, namely to build a scaffolding according to the requirement of the zone of proximal development; to create a problem solving scenario to generate a cognitive gap; to guide students to independently explore knowledge construction with the help of the scaffolding built by the teacher; to strengthen knowledge construction through the communication between student–student and teacher-student; and to adopt multi-dimensional evaluation for diagnostic and reflective learning.

Activity Theory and its implications for blended learning

Activity Theory was proposed by Lev Vygotsky and enhanced by former Soviet Union psychologists Alexei Nikolaevich Leontyev and Alexander Romanovich Luria. It is the product of sociocultural activity and sociohistorical research. Activity theory emphasizes the bridging role of activities in the process of internalization of knowledge and skills. Activities are the basis of psychology, particularly for the occurrence and development of human consciousness, while human activities are objective and social.

In instructional design, the subjects are the students and the object is learning objectives, which are affected and changed by the subjects through certain activities. The community refers to other common learners except the students themselves, such as teachers, classmates, other personnel, etc. The tools can be understood as part of the instructional environment, including the design of hardware and software used in the instructional process. The rules are used to coordinate subject and community, as restrictions or agreement in teaching and learning activities. The division of labor means that different members must complete different tasks in the teaching and learning process.

According to activity theory, blended learning should fully utilize technology as a “tool” to help students to achieve their learning goals. At the same time, the rules that need to be followed should be fully designed in the learning activities involving community and individuals; if there are tasks, the division of labor should be well designed to ensure the effectiveness of blended learning.

Community of Inquiry

In 1999, the theoretical framework of the Community of Inquiry for the online learning environment was proposed by Randy Garrison, Terry Anderson, and Walter Archer of the University of Alberta in Canada (Garrison et al. 1999 ).

There are three core elements of teaching and learning in the Community of Inquiry theory proposed by Garrison et al.: cognitive presence (Garrison et al. 2001 ), social presence (Rourke et al. 1999 ) and teaching presence (Anderson et al. 2001 ). Garrison specified the categories and indicators of the three presences (Garrison and Arbaugh 2007 ).

The focus of the community of inquiry is the creation of educational experiences, which need to adhere to the following eight principles: first, establish purposeful and active inquiry activities. Second, plan for preparing critical thinking through critical reflection and discourse. Third, plan for building trust and creating an atmosphere that supports open communication. Fourth, establish a learning community and form cohesion. Fifth, establish mutual respect and responsibility. Sixth, plan the curriculum content, learning method, and learning time, and effectively monitor and manage critical dialogues and collaborative reflection activities. Seventh, sustain inquiry that moves to a resolution. Eighth, ensure assessment is congruent with intended processes and outcomes.

The following section elaborates on the three community of inquiry presences. Cognitive presence refers to the learners’ ability to construct and confirm meaning on their own through continuous reflection and discourse (Anderson et al. 2001 ). It includes four phases, namely triggering events, exploration, integration, and resolution (Garrison and Arbaugh 2007 ). Specifically, the process consists of a problem or task designed to trigger students for further inquiry; exploration for relevant information or knowledge, analysis, and integration of different perspectives and understandings; and solutions to the problems (Garrison et al. 2000 ).

Teaching presence refers to the design, facilitation, and guidance of cognitive and social process so that learners can achieve personally and educationally meaningful learning outcomes. According to Anderson et al. ( 2001 ), there are three categories in teaching presence, namely, instructional design and organization, discourse facilitation, and direct instruction. The main tasks of teaching presence are to create curriculum content; design learning activities and methods; set learning activity sequences; effectively use communication media; organize, accommodate, and manage purposeful critical dialogues and collaborative reflection activities; and provide students with timely feedback. Teaching presence encourages learners to become investigators with metacognitive awareness and metacognitive strategies in collaborative inquiry.

Social presence refers to the learners’ ability to project themselves socially and emotionally, thereby being perceived as “real people” in mediated communication (Short et al. 1976 ). Social presence consists of three components: affective expression, open communication, and group cohesion.

According to the theoretical model of the Community of Inquiry, the three elements are interrelated and reciprocally influence each other. Partially overlapping the three elements will generate meaningful educational experiences.

The revised CoI framework introduced three external factors apart from communication medium, namely educational context, discipline standards, and applications (Garrison, 2016 ).

Critical thinking is one of the ultimate goals of higher education. The CoI framework can play an important role in the development of students’ critical thinking skills in blended learning. Teachers need to understand and make full use of the three elements of CoI, facilitate in-depth and meaningful learning through collaborative learning activities and reflective dialogues involving critical thinking, and help to achieve student development goals.

1.4 Models of Blended Learning

The previous sections have first discussed the psychological and pedagogical basis for the emergence and development of blended learning, then elaborated on the theoretical foundations (system theory, educational communication theory, learning theory, and curriculum theory) for designing ideal blended learning. In this section, the theory of blended learning will be illustrated, including the blended learning model (namely the plan of blended learning based on theoretical foundations), the instructional design process model of blended learning (namely the process of designing blended learning), and the practice model of blended learning, which explains how to blend in practice. This section will first elaborate on the blended learning model.

A instructional model refers to a relatively stable interaction relationship among various instructional elements designed and gradually developed through practice and guided by educational philosophy that aims to achieve specific learning objectives. Such a model includes the integration of various elements of instructional process, instructional procedures, and corresponding strategies and evaluation methods.

Next, several commonly used instructional models and their applications in blended learning will be introduced. Designers can adopt a model according to their own instructional scenarios (such as learning objectives, learning content, and learners). It should be noted that the following instructional models can be applied in face-to-face, online learning, as well as online and offline learning.

1.4.1 Cognitive Apprenticeship Instruction

Before formal schooling, apprenticeship used to be the most common learning approach. In the ancient apprenticeship system, the apprentice observed the master’s work, communicated with the master, and tried to do the work. After having feedback from the master, the apprentice reflected and gradually developed skills that were as good as the master’s. This method enables learners to put into practice what they have learned because learning occurs in the real world. After the establishment of schools, and as learning has gradually been separated from real-life situations, it is difficult to develop learners’ higher-order cognitive abilities, such as applying knowledge to problem solving. To remedy the defects of traditional education, Collins et al. first proposed Cognitive Apprenticeship Instruction in 1989 (Collins et al. 1988 ). “Apprenticeship” indicates its inheritance or similarity with the traditional apprenticeship system, emphasizing that learning should take place in application scenarios, and knowledge and skills are acquired through the integration of observation of expert work and practice; “cognition” reflects a relatively strong practical significance, emphasizing that learning of generalized knowledge occurs in application scenarios to facilitate the application of knowledge in various scenarios. Cognitive apprenticeship aims to develop learners’ higher-order cognitive skills, such as problem-solving and reflective skills.

Six operational strategies for Cognitive Apprenticeship Instruction are recommended as follows: modeling, coaching, scaffolding, articulation, reflection, and exploration. Five socialization strategies are also recommended: situational learning, simulation, community of practice, intrinsic motivation, and cooperation.

Cognitive apprenticeship can be implemented in face-to-face learning. However, when the class size is relatively large, learning effectiveness will be compromised. Therefore, blended learning has more advantages in terms of providing scenarios, demonstrating through technical means, providing scaffolding, individual tutoring, and showing students’ practice.

1.4.2 Resources-Based Learning

Resources-based learning refers to an instructional model in which learners learn by interacting with a wide range of learning resources instead of attending classes. The learning resources refer to all available print and non-print media, including books and articles, audio-visual materials, electronic databases, and other computer-based, multimedia, and Internet-based resources. This instructional model aims to develop learners’ ability to learn or explore independently. At the same time, resources-based learning allows learners to choose the methods and pace they prefer to solve the same problem, with the flexibly to make adjustments according to their learning styles, interests, and competences. Therefore, the learning style with this instructional model is individualized or personalized. Resources-based learning usually involves the following steps:

Identify the problem. Key points of courses need to be changed to questions and learning objectives that students can explore.

Identify methods for collecting information. Students need instruction and guidance on information collection and the exploration of potential sources of information.

Collect information. During the process of information collection, students are required to be able to identify and select important information or facts relevant to research questions and to classify the information collected.

Use information. Students need to be instructed on how to use the information they have collected, and how to take note of sources of information.

Synthesize information for solving problems and present. Students are guided to organize information into a systematic, logical synthesis in order to solve the problem. Afterwards, students are required to present in oral, written, or other forms to demonstrate how they synthesized information to solve problems.

Evaluate. Students need to be provided the opportunity for evaluation and understanding on how to evaluate what they have completed. Evaluation and self-reflection are the highlights of resources-based learning (Awaludin 2019 ).

It can be argued that blended learning has advantages in implementing resources-based learning.

1.4.3 Project-Based Learning

Project-based learning (PBL) is a situational learning method based on constructivist theory (Lave and Wenger 1991 ). To be specific, when students actively build understanding by fully grasping concepts and applying them to real-world situations, they are able to acquire a deeper understanding of the learning materials. PBL is usually project-driven and requires students to integrate their understanding across disciplines. PBL projects must integrate fragmented discipline problems to address practical challenges and complex problems.

PBL is mainly composed of four core components, namely content, activity, context, and result. The implementation process is generally divided into six steps, namely learning relevant knowledge, selecting projects that comprehensively apply knowledge to create products (e.g., artifacts, schemes, etc.), forming groups, working in groups to complete projects guided by teachers, sharing project results, and conducting project evaluation. Steps can be added or deleted according to the implementation status of the projects. Blended learning can provide a better learning environment for the implementation of project-based learning.

1.4.4 Problem-Based Learning

Problem-based learning (PBL) emphasizes setting learning in complex and meaningful problem contexts. Students can collaboratively address real-world problems to acquire scientific knowledge, develop problem-solving skills, and become autonomous learners (Hmelo and Ferrari 1997 ). The steps of the PBL process are as follows (Chang et al. 2020 ):

Students are introduced to an ill-structured problem related to their real life.

Students collaboratively analyze the problem and discuss what they need to learn to solve the problem.

Students work on their own to find resources.

Students meet and discuss with group members regarding whether they can have a feasible solution based on the resources they found, which may need several rounds of attempts.

Students report the process of learning and problem solving in front of the whole class.

Students reflect and evaluate the whole process with their teacher and peers.

When it comes to blended learning, problem-based learning has the following benefits for students:

the construction of a more authentic problem context

timely communication and collaboration anytime, anywhere

autonomous learning based on resources

online Q&A areas for questioning and feedback

timely feedback for supporting their inquiry process.

1.4.5 Distributed Learning

Distributed learning, as an instructional model, allows teachers, students, and teaching content to be distributed in a decentralized way, so that teaching and learning can take place at different times and locations. The characteristics are:

distributed learning resources, which enable students to access good instructional resources everywhere

learner-centered, which means students can choose instruction resources suitable for themselves

collaboration and communication through interactive community, which enables students to acquire learning skills and social skills

knowledge construction through learning experience in the virtual environment (Li et al. 2007 ; Zhong and Zhang 2005 )

Distributed learning can overcome the time and space limitations of traditional classrooms and expand the circle of teachers and peers. In other words, students can access instructional resources and resource persons on the Internet so that students can have cognitive views and experiences that are different from those locally to support their knowledge and meaning construction. Students can learn anytime, anywhere based on their needs. A blended learning environment is more conducive to the implementation of a distributed learning model.

1.4.6 Random Access Learning

Random access learning is an instructional model in which complex (or advanced) knowledge and skills are taught. Students can learn the same content several times for different purposes, through different channels, from different aspects, and with different methods, so as to gain a multi-faceted understanding (Spiro et al. 1995 ).

Due to the complexity and multi-faceted nature of problems in some fields, it is difficult to have a comprehensive understanding of the inherent nature of these complex problems and the interrelationships between things from one perspective or at one time. Therefore, random access learning advocates multiple attempts at the learning content for different purposes and from different perspectives. As a result, learners can have a qualitative leap in gaining a comprehensive understanding of the problem. In other words, learners will be able to improve their ability to understand complex interrelationships between things and flexibly apply the knowledge and skills acquired.

Random access learning, based on constructivist theory, helps to develop learners’ creativity and encourages their communication and cooperation. It is applicable in learning contexts including ill-structured and complex problem-solving scenarios, which focus on knowledge application and transfer.

Blended learning makes it more convenient to implement random access learning. Face-to-face learning is suitable for learners to share opinions about one issue from different angles, while online learning enables students to approach the same content from multiple angles by providing learning resources that involve different viewpoints of the same content. At the same time, students can take advantage of the discussion area on the platform to present and share their own opinions with others, which can promote the effective implementation of random access learning.

1.4.7 Flipped Classroom

The educational philosophy behind the flipped classroom is active learning, the core of which is to reverse the activities that take place inside and outside the classroom. That is, “the events that have traditionally taken place inside the classroom now take place outside the classroom and vice versa” (Lage and Treglia 2000 ).

Alten et al. ( 2019 ) summarized five advantages of flipped classrooms by reviewing the existing literature:

It requires students to have strong self-regulation learning ability and in turn helps to develop students’ self-regulation ability.

Students take the initiative to complete assignments in class with the help of teachers, avoiding cognitive overload when completing homework alone.

Students have more time for learning activities, which is an active, constructive, and interactive mode of participation.

Students have more opportunities to receive effective feedback and differentiated instruction from their teachers.

Finally, FTC is often assumed to be a promising pedagogical approach that increases student satisfaction about the learning environment.

Criticisms of flipped classroom include:

Video production is too time- and energy-consuming for teachers and places an excessive demand on learners’ abilities.

It takes up too much time after class and causes too much pressure on students.

The design framework of flipped classrooms should consist of 9 principles (Kim et al. 2014 ), specifically:

provide an opportunity for students to gain first exposure prior to class

provide an incentive for students to prepare for class

provide a mechanism to assess student understanding

provide clear connections between in-class and out-of-class activities

provide clearly defined and well-structured guidance

provide enough time for students to complete assignments

provide facilitation for building a learning community

provide prompt/adaptive feedback on individual or group works

provide technologies that are familiar and easy to access.

Teachers can refer to this framework to design flipped courses. The flipped classroom is a typical model of blended learning, which aims to establish an organic systematic integration between online and face-to-face learning activities.

1.5 Blended Learning Models and Blended Learning Practice Models

To realize the above-mentioned instructional models of blended learning and their learning objectives, the implementation process needs to be designed. From this design process is formed the design process model of blended learning (simply named the blended learning design model). The blended learning design model follows the common characteristics of general instructional design models and has its own uniqueness. This section first discusses the general learning design model to be followed during blended learning, then examines the special characteristics of blended learning design, discusses the blended learning design models that are based on these specific characteristic, and introduces some practice models of blended learning i.e., how to blend in practice.

1.5.1 Blended Learning Design Model

According to the levels of the system, the instructional design can be divided into three levels: “system centered” (such as professional program plans, curriculum, etc.), “product centered” (such as instructional software and resources, etc.) and “classroom centered” (from a unit that takes several hours of classroom teaching). The instructional design at the three levels share common elements of design, but the emphasis at different levels varies.

By summarizing thousands of models of instructional design, Wu ( 1994 ) formed the general model of classic instructional design. This model is designed based on learning theories, instructional theories, communication theories, and systems theories, including all elements of the instructional design process in a relatively systematic and holistic manner.

The general learning design model should be followed for blended learning design. In terms of different levels, blended learning has the design of a blended learning platform and blended courses for system-focused level, as well as a blended instruction process (several hours of classroom teaching) that is at a classroom-focused level. The development of a variety of instructional resources is required in blended learning, at the product-focused level. All elements of the general learning design model are also involved in blended instructional design.

At the same time, the particularity of blended learning also needs to be considered, compared with pure offline or online instruction, which should be the most important difference between the blended instructional design model and the general model of instructional design.

Compared with traditional face-to-face learning, blended learning extends and widens the teaching and learning time and space. Students can learn independently or cooperatively at anytime and anywhere at their own pace. At the same time, instructors are more likely to obtain a variety of relevant information about student learning and provide a personalized learning environment that is more appropriate for them by tracking their online learning traces with the support of big data and learning analytics. Compared with online learning, blended learning would dissolve the sense of alienation between teachers and students and the sense of isolation of students, offer a more flexible organization of teacher-student meetings, allow for group teaching of content suitable for face-to-face instruction, and thus increase the effectiveness of teaching and learning.

Stein/Graham’s design model of blended learning

Jared Stein and Charles R. Graham co-authored a book on blended learning, Essentials for blended learning: a standards-based guide , in 2014. They proposed the blended course design model when conducting research on blended learning in colleges and universities. Blended course design is a cyclic process consisting of three activities: designing, engaging, and evaluating. Stein and Graham emphasized that blended classrooms need iterative development and pointed out that the design of individual learning activities, courses, or units can be supported through continuous improvement of the three activities.

The design part of the model includes four components: designing learning goals and objectives, designing evaluation and feedback, describing learning activities that can achieve teaching goals, and adding online elements to the learning process. The process starts with learning outcomes, then designs corresponding assessment tasks, and finally creates activities for achieving learning outcomes. Teachers are encouraged to design a small part (such as a single course or unit) at a time, and further optimize the design after students have participated in blended learning activities and evaluated online and offline learning effectiveness. In order to support the implementation of instructional design, Stein and Graham ( 2014 ) provided a blended course design template in the book Essentials for blended learning: a standards-based guide.

Eagleton’s design model of blended learning

Eagleton ( 2017 ) proposed a blended learning intervention design model for the teaching of psychology in higher education, including three parts: identifying learning task needs (through pre-tests, learning outcomes, and student profile analysis), designing learning interventions (including developing and disseminating instructional strategies, learning strategies, and evaluation strategy) and evaluation.

Determining learning task requirements is divided into three sub-tasks: obtaining students’ personal profiles, pre-testing students, and specifying learning outcomes, i.e., learning objectives

Finalizing learning task needs based on basic student learning information and learning objectives, followed by learning intervention design (teachers’ teaching strategies, students’ learning strategies, forms of instructional content development and dissemination, and design of instructional evaluation); and

Evaluation (mainly feedback and slight adjustment of instructional programs in the implementation process).

In addition, Eagleton et al. argued that creating a blended learning program is a process that needs to take into account the ability of teachers, the infrastructure of the institution, and the learners’ acceptance of the new learning mode. The blended learning design can be integrated into the whole-brain learning model (Eagleton and Muller 2011 ). It is necessary to consider the differences in students’ information processing of the left brain and the right brain when learning different materials, and integrate the whole-brain learning model to design the learning content and learning form.

1.5.2 Blended Learning Practice Models

In blended learning, one problem to be faced is when to implement online learning and when to implement offline learning. Through blended learning and teaching practices, scholars have summarized some blended learning practice models. These models can help teachers to increase their knowledge and experiences of how to blend online and offline learning.

Zhu Zhiting’s three models of blended learning practice

Zhu and Hu ( 2021 ) summarized three models of blended learning practice, namely O2O, OAO, and OMO, and pointed out that blended learning would inevitably move towards OMO in the future.

O2O (Online to Offline) model refers to a learning environment based on online, offline, or online-to-offline practice. The teaching process mainly takes place offline, while an online learning environment serves as the triage. In a flipped classroom, the ‘students’ online learning determines offline teaching’ model is an embodiment of this type of triage. Another example is maker learning, in which tasks are provided online and explored offline. Learning in this model is mainly a one-way flow from online to offline. There are clear boundaries between online and offline.

OAO (Online and Offline) model refers to the integrated ‘dual store’ form that integrates online and offline practice organically. It is a model based on the integration of online and offline practice, with two-way online and offline intercommunication, interconnection, and mutual appreciation. There are clear boundaries between online and offline.

OMO (Online Merge Offline) model refers to a student-centered learning environment based on the comprehensive integration of online and offline practice, synchronously and asynchronously. Technical methods are used to bridge various structures, levels, and types of data online and offline; establish an ecology of online and offline merging through virtual and real learning scenarios; and realize a new teaching style of personalized teaching and service. This kind of environment is developing in the direction toward the ‘physicalizing of online space and virtualization of offline space’. There are two important changes in the development direction of the learning environment constructed by OMO: 1. the interface boundaries between online and offline is weakening and disappearing; 2. the learner has changed from “marching with heavy burdens” to “walking with ease”.

Michael Horn’s six blended learning models

Micaeal Horn of the Innosight Institute in the United States has identified six blended learning models (Horn and Staker 2011 ).

Face to face driven model is a blended learning model mainly based on face-to-face learning. Teachers deliver course content through traditional classroom teaching, supplemented by online course resources or review materials, so that students can learn independently at home, in the classroom, or the laboratory. Another approach to this model is for teachers to allow students to learn online course content at their own pace in the classroom. The role of the teacher in the process is to provide individualized instruction.

Online driven model is mainly based on online learning. Students primarily study online at a distance, with the option to attend face-to-face instruction. This model provides all learning content through dynamic management of online courses and uses remote synchronous interactive systems (such as video conferencing systems) or asynchronous interactive systems (such as BBS discussion areas) to conduct Q&A discussions with individual students and groups. This model can provide students with learning opportunities at any time and any place and provides more choices for students’ extracurricular activities.

Face-to-face and online rotation model : alternating between face-to-face and online instruction, students alternate between a period of face-to-face instruction and a period of online learning outside of the classroom. A flipped classroom is a form of this model, where students learn online course content in advance, at home and then come to the classroom to receive face-to-face instruction from teachers.

In the flex model , learning content happens online or face-to-face in groups/individual tutorials. Most students learn in an online environment. They can receive face-to-face instruction in the classroom, but the face-to-face instruction is only for group or individual tutoring. Students decide how to arrange the learning content and construct their knowledge at their own pace. While students can access course resources through mobile devices at home and in school or anywhere, teachers play a key role in facilitating learning with individuals and groups of students in a brick-and-mortar school. This model requires obtaining information through the Internet, no matter where the students are located.

Online lab model refers to learning in the computer lab and completing the interactions online. All course materials and teaching activities are completed in the computer lab. Students are supposed to learn independently by watching multimedia learning materials and interacting synchronously or asynchronously with teachers or classmates through video conferencing systems, forums, and e-mails. Although this model sets up a complete online course for students to learn at their own pace, the learning process is in the space of a brick-and-mortar school. Most course units are completed by students on their own, while some units require the collaboration of study groups of three to four students.

The self-blended model allows students to choose between online and offline learning. This is a personalized instructional model. Students can choose learning content online and learn based on their learning needs. Most of the learning is completed online, but students can participate in face-to-face classroom instruction. In the implementation of this model, to support students on the acquisition of relevant knowledge and learning tools, teachers need to prepare corresponding online courses in advance as necessary resources for students to complete learning tasks.

Alten, D. V., Phielix, C., Janssen, J., & Kester, L. (2019). Effects of flipping the classroom on learning outcomes and satisfaction: a meta-analysis. Educational Research Review, 28 , 1-18.

Google Scholar  

Anderson, T., & Dron, J. (2011). Three generations of distance education pedagogy. International Review of Research in Open and Distance Learning, 12(3) , 80-97.

Anderson, T., Rourke, L., Garrison, D. R, & Archer, W. (2001). Assessing teaching presence in a computer conferencing context. Online Learning, 5(2).

Awaludin, A. (2019). Resource based learning for teaching Arabic. Ijaz Arabi Journal of Arabic Learning, 2(1).

Bersin, J. (2004). How Did We Get Here? The History of Blended Learning. Chapter one of The Blended Learning Book: Best Practices, Proven Methodologies, and Lessons Learned. New Jersey: Wiley.

Cabrero, R. S., & Román, O. C. (2018). Psycho-pedagogical Predecessors of Connectivism as a New Paradigm of Learning. International Journal of Educational Excellence, 4(2), 29-45.

Carreño, I. (2014). Theory of connectivity as an emergent solution to innovative learning strategies. American Journal of Educational Research, 2(2) , 107-116.

Chang, N., Wang, Z., & Hsu, S. H. (2020). A Comparison of the Learning Outcomes for a PBL-based Information Literacy Course in Three Different Innovative Teaching Environments. Libri, 70(3), 213–225.

Chen Q., & Liu R. (2019). Contemporary Educational Psychology (in Chinese) . 3rd Edition. Beijing: Beijing Normal University Press.

Collins, A., Brown, J. S., & Newman, S. E. (1988). Cognitive apprenticeship: Teaching the craft of reading, writing and mathematics. Thinking: The journal of philosophy for children, 8(1) , 2–10.

Collins, A., & Halverson, R. (2009). Rethinking Education in the Age of Technology: The Digital Revolution and Schooling in America. Technology, Education--Connections (TEC) Series. New York: Teachers College Press.

Dewey, John. (1963). Experience and Education. New York: Collier Books, Print.

Dewey, John. (2001). Democracy and Education. Penn State Electronic Classics Series Publication.

Dou X. (1988). Introduction to System Theory (in Chinese). Ningxia Education, (4) , 10-11.

Duan M., Pei X., & Li X.(2009). The Paradigm Shift of Educational System——A Dialogue with Prof. Charles M. Reigeluth, an International Instructional Design Expert (in Chinese). China Educational Technology, (5), 1-6.

Duke, B., Harper, G., & Johnston, M. (2013). Connectivism as a Digital Age Learning Theory. The International HETL Review , Special Issue: 4–13.

Downes, S. (2005). Feature: E-learning 2.0. E-learning magazine, (10) , 1.

Downes, S. (2012). Connectivism and Connective Knowledge: essays on meaning and learning networks. National Research Council Canada.

Eagleton S, Muller A. (2011). Development of a model for whole brain learning of physiology. Adv Physiol Educ, 35, 421–426.

Eagleton S. (2017). Designing blended learning interventions for the 21st century student. Adv Physiol Educ, 41(2) , 203-11.

European commission. (2018). Proposal for a Council Recommendation on Key Competences for Lifelong Learning.

Erikson, Erik H. Childhood and society, 2nd ed., 1964. W.W. Norton & Co. New York: Cambridge. University Press.

Gardner, H. (1993). Multiple intelligences. Basic books.

Garrison D R. (2016). Thinking collaboratively: Learning in a community of inquiry . New York: Routledge.

Garrison, D. R., & Arbaugh, J. B. (2007). Researching the community of inquiry framework: Review, issues, and future directions. The Internet and higher education, 10(3), 157-172.

Garrison, D. R., Anderson, T., & Archer, W. (1999). Critical Inquiry in a Text-Based Environment: Computer Conferencing in Higher Education. The Internet and higher education, 2(2) , 87-105.

Garrison, D. R., Anderson, T., & Archer, W. (2000). Critical inquiry in a text-based environment: Computer conferencing in higher education. The Internet and Higher Education, 2(2-3) , 87−105.

Garrison, D. R., Anderson, T., & Archer, W. (2001). Critical thinking, cognitive presence, and computer conferencing in distance education. American Journal of Distance Education, 15(1), 7-23.

Gao Y.X. (1989). Personality Psychology (in Chinese) . Beijing: Beijing Normal University Press.

Gharacheh, A., Esmaeili, Z., Farajollahi, M., & Jamaizadeh, M. (2016). Presentation of blended learning conceptual pattern based on individual and social constructivism theory. Int., J. of Humanities and Cultural Stud. , 1.

Horn, M. B., & Staker, H. (2017). Blended: Using disruptive innovation to improve schools. John Wiley & Sons.

Horn, M. B., & Staker, H. (2011). The rise of K-12 blended learning. Innosight institute, 5(1) , 1-17.

Hmelo, C. E. & Ferrari (1997). The problem-based learning tutorial: Cultivating higher order thinking skills. Journal of the education of the gifted, 20 (4), 401- 422.

Hung, D. (2001). Design principles for web-based learning: Implications from Vygotskian thought. Educational Technology, 41 (3), 33-41.

Kagan, J., Rosman, B., Day, D., & Albert, J. (1964). Information processing in the child: Significance of analytic and reflective attitudes. Psychological Monographs,78(1), 1-37.

Kim, M. K., Kim, S. M., Khera, O., & Getman, J. (2014). The experience of three flipped classrooms in an urban university: An exploration of design principles. The Internet and Higher Education, 22, 37-50.

Lage, M. J., Platt, G. J., & Treglia, M. (2000). Inverting the Classroom: A Gateway to Creating an Inclusive Learning Environment. The Journal of Economic Education, 31(1), 30.

Lam, J. (2015). Collaborative learning using social media tools in a blended learning course. In International conference on hybrid learning and continuing education (pp. 187–198). Springer, Cham.

Lave, J., & Wenger, E. (1991). Situated learning: legitimate peripheral participation. Li L.(2010). Instructional Design (in Chinese) . Beijing: Higher Education Press.

Li Y., Jiang W. & Yao Q.(2007). A Brief Discussion on Distributed Learning (in Chines(e). Modern Educational Technology, (1), 52–54, 14.

Lin C. (1997). On the Construction of Subject Competence (in Chinese). Journal of Beijing Normal University (Social Science Edition), (1) , 5-12.

Liu M., Kang C., Dong L. (2018). Instructional design research: A disciplinary perspective (in Chinese). Beijing: Beijing Normal University Press.

Nan G., Li Y. (2005). Educational Communication (Second Edition) (in Chinese) . Beijing: Higher Education Press.

Maslow, A. H. (1970). Motivation and personality (2nd ed.) . New York: Harper & Row.

Merrill, M. D. (2002). First principles of instruction. Educational technology research and development, 50, 43-59.

Ormrod, J. E. (1999). Human learning. Pearson Education.

Otiende, J. E., & Sifuna, D. N. (1994). An introductory history of education. Nairobi: University of Nairobi Press.

Piaget, J. (1976). Piaget’s Theory. In B. Inhelder, H. H. Chipman, & C. Zwingmann (Eds.), Piaget and His School: A Reader in Developmental Psychology (11-23). Springer Berlin Heidelberg.

Piaget, J. (1997). The principles of genetic epistemology (Vol. 7) . Psychology Press.

Research Group on Core Literacy. (2016). Core Competencies and Values for Chinese Students (in Chinese). Journal of The Chinese Society of Education, (10), 1-3.

Rogers, C. R. (1982). Freedom to learn (2nd ed.) . Ohio: Merrill.

Rogers, C. R., Lyon, H. C., & Tausch, R. (2012). On becoming an effective teacher: Person-centered teaching, psychology, philosophy, and dialogues with Carl R. Rogers and Harold Lyon . London: Routledge.

Rourke, L., Anderson, T., Garrison, D.R. & Archer, W. (1999). Assessing Social Presence in Asynchronous Text-based Computer Conferencing. The Journal of Distance Education / Revue de l’ducation Distance, 14(2) , 50-71.

Shaffer, D. R. & Kipp, K. (2012). Developmental psychology: Childhood and adolescence (9th ed). Wadsworth Cengage Learning.

Short, J., Williams, E., & Christie, B. (1976). The social psychology of telecommunications : London; New York: Wiley.

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of Instructional Technology & Distance Learning, 2(1) , 3-10.

Singh, H., & Reed, C. (2001). A white paper: Achieving success with blended learning. Centra software , 1 , 1-11.

Spiro, R. J., Feltovich, P. J., Jacobson, M. J., & Coulson, R. L. (1995). Cognitive flexibility, constructivism, and hypertext: Random access instruction for advanced knowledge acquisition in ill-structured domains. Constructivism, Hillsdale , NJ: Erlbaum, 9 .

Steffe, & J. E. Gale (Eds.), Constructivism in education; Constructivism in education (pp. 85–107, Chapter xvii, 575 Pages). Lawrence Erlbaum Associates, Inc, Hillsdale, NJ.

Stein, J., & Graham, C.R. (2014). Essentials for blended learning : a standards-based guide. New York: Routledge.

Strozier, R. M. (2002). Foucault, subjectivity, and identity: Historical constructions of subject and self. Wayne State University Press.

Toffler A. (1990). Powershift: Knowledge, wealth, and violence at the edge of the 21st century. Bantam Books.

Voogt, J, Roblin N. (2012). A comparative analysis of international frameworks for 21st century competences: Implications for national curriculum policies. Journal of Curriculum Studies, 4(3), 299-321.

Vygotsky, L. S. (1980). Mind in society: The development of higher psychological processes. Cambridge, MA: Harvard University Press.

Wang J., Yuan S. & Zhao G.(2018). The impact of blended teaching on college students’ learning effectiveness: An empirical study based on the application effect of MOOC in first-class universities in China (in Chinese). Modern Distance Education, (5), 39-47.

Wang S. (2017). Rain Classroom: Smart Teaching Tools in the Background of Mobile Internet and Big Data (in Chinese). Modern Educational Technology, (5), 26-32.

Watson, J. B., & Kimble, G. A. (2017). Behaviorism. Routledge.

Wei Q., & Zhong Z.(1992). Educational Communication (in Chinese) . Nan Chang: Jiangxi Education Press.

Wei Y., Qin D., Hu J., Yao H. & Shi Y.(2019). Classroom action recognition of students based on deep learning (in Chinese). Modern Educational Technology, (7), 87-91.

Witkin, H. A., Moore, C. A., Goodenough, D. R., & Cox, P. W. (1977). Field-dependent and field-independent cognitive styles and their educational implications. Review of educational research, 47(1) , 1-64.

Wu M.(1994). Instructional Design. Beijing: Higher Education Press.

Yin H., & Bi H.(2000). Learning Ability (in Chinese) . Qingdao: Qingdao Ocean University Press.

Zhang Xiaofeng. (2002). Curriculum Development under the consideration of multiple intelligences theory (in Chinese) . Journal of Educational Development, (1), 20-23.

Zhang Y., Yi D., Liu L., Wu Y., Liu X., Zhao Z. & Cao Y.(2019). Construction and practice of blended teaching mode based on SPOC (in Chinese). Chongqing Medicine, (21), 3766-3769.

Zeng W., & Cao R. (2005). Analysis of Learning Subjects (in Chinese) . Beijing: Chinese Cultural and Historical Press.

Zhong Z., & Zhang Q.(2005). On Distributed Learning (in Chinese). Studies In Foreign Education, (7), 28-33.

Zhu Z. & Hu J. (2021). Technology empowers educational innovation in the later stage of the epidemic: a new form of online and offline integrated teaching (in Chinese). Open Education Research, (1), 13-23.

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• Published: 08 July 2023

Adoption of blended learning: Chinese university students’ perspectives

• Teng Yu 1 , 2 ,
• Jian Dai 3 , 4 &
• Chengliang Wang 4 , 5  

Humanities and Social Sciences Communications volume  10 , Article number:  390 ( 2023 ) Cite this article

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Against the backdrop of the deep integration of the Internet with learning, blended learning offers the advantages of combining online and face-to-face learning to enrich the learning experience and improve knowledge management. Therefore, the objective of this present study is twofold: a. to fill a gap in the literature regarding the adoption of blended learning in the post-pandemic era and the roles of both the technology acceptance model (TAM) and the theory of planned behavior (TPB) in this context and b. to investigate the factors influencing behavioral intention to adopt blended learning. For that purpose, the research formulates six hypotheses, incorporates them into the proposed conceptual model, and validates them using model-fit indices. Based on data collected from Chinese university students, the predicted model’s reliability and validity are evaluated using structural equation modeling (SEM). The results of SEM show that (a) the integrated model based on the TAM and the TPB can explain 67.6% of the variance in Chinese university students’ adoption of blended learning; (b) perceived usefulness (PU), perceived ease of use (PEU), and subjective norms (SN) all have positive impacts on learning attitudes (LA); (c) PEU has a positive influence on PU, and SN has a positive influence on perceived behavioral control (PBC); and (d) both PU and LA have a positive influence on the intention to adopt blended learning (IABL). However, PEU, SN, and PBC have little effect on IABL; e. LA mediates the effect of PU on IABL, and PU mediates the effect of PEU on IABL. This study demonstrated that an integrated conceptual framework based on the TAM and the TPB as well as the characteristics of blended learning offers an effective way to understand Chinese university students’ adoption of blended learning.

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Introduction

Due to the spread of COVID-19, certain conventional face-to-face teaching methods became inappropriate for the current teaching situation. By July 2020, more than 180 countries had closed their schools due to the outbreak. Worldwide, online learning offerings were also reevaluated to meet the difficulties of the global educational environment (UNESCO, 2020 ). As a consequence of the hazards that COVID-19 posed to teaching and learning, students were compelled to shift from face-to-face to online learning (Yu et al., 2021 ), with the majority of courses in China opting for blended learning (Kang et al., 2021 ). In February 2020, China became the first country to announce the launch of online courses. The question of whether online learning could replace traditional offline education has sparked heated debate in China (Jin et al., 2021 ). Moreover, the Ministry of Education of China issued an announcement in 2021 claiming that it was essential to accelerate the development of new infrastructure, such as intelligent teaching spaces or campuses, as well as to promote blended learning (Yang et al., 2022 ). Online learning was adopted in China, reducing the consequences of school shutdowns across the country and slowing the virus’s spread. Nevertheless, the government was required to address issues concerning what to educate, how to teach, and how to provide fundamental necessities such as education infrastructure. The Chinese Ministry of Education offered a variety of teaching platforms that allowed students to take online lessons via their laptops, desktop computers, cell phones, etc. (Zhang et al., 2020 ).

Driven by the rapidly changing digital ecosystem, the design and application of blended teaching modes have become important components of the reform of teaching methods in colleges and universities. Blended learning refers to the organic integration of online and offline learning, which may not only guide and inspire students’ learning but also arouse students’ enthusiasm and autonomy with respect to learning. In addition, Pulham and Graham ( 2018 ) identified the top 20 blended teaching skills. Many elements impact students’ reactions when these technologies are utilized in the learning process (Xu et al., 2021 ). Because the actors involved in educational process change, these frameworks undergo constant alteration. Rapid changes in users’ digital abilities and attitudes toward technology can be observed (Lazar et al., 2020 ).

Since that time, the COVID-19 pandemic has exhibited the ordinary trend of ups and downs. Hence, the pandemic has continued for a long period and thus had a substantial influence on many parts of society, particularly education (Cahapay, 2020 ). The extensive application of online teaching has led to many problems, such as low satisfaction with the teaching effect, low willingness to continue using this type of teaching, and instructional techniques that must be enhanced. Consequently, when users’ expectations are met, their contentment increases (Cheng et al., 2019 ; Kim, 2010 ); conversely, if their perceived performance falls short of what they had expected, their satisfaction decreases (Mellikeche et al., 2020 ). However, Popa et al. ( 2020 ) proposed that offline education provides the benefits of real-world experience, ease of engaging in diverse activities, cultural value exchange, and simple management and service, which are absent in online education. As a result, the present model of education is gradually evolving, and the mix of online and offline instruction is leading to a major educational revolution.

The inclusion of technology in face-to-face education has aroused a great deal of interest and has opened up several areas of study over the years. Because of its perceived efficiency in offering flexible, timely, and continual learning, blended learning is currently widely regarded as the most popular and effective instruction mode used in educational institutions. Students must adjust to new blended learning techniques and environments with the assistance of modern technologies (Mo et al., 2022 ). In Chinese universities, faculty should be encouraged to develop courses based on the features of their local institutions during the post-pandemic phase to decrease the learning costs associated with blended learning and the time required to install e-learning platforms. As a result, the teaching effect of blended learning can be enhanced (Lin et al., 2021 ).

Blended learning has received significant attention and has been widely recognized as “the new normal” based on several influential studies (Dziuban et al., 2018 ). These studies have highlighted the numerous advantages associated with blended learning. Students, for example, are expected to manage and complete their studies independently of their teacher and at their own pace, so they must have self-regulation abilities and technological proficiency when utilizing online technology outside of their offline meetings. Moreover, to properly utilize and operate technology in teaching as well as develop and post learning materials to students, instructors must be technologically savvy (e.g., with regard to creating quality online videos). Furthermore, educational institutions must offer both instructors and students essential training and technical help to facilitate the successful use of existing technologies as well as the efficient use of the online component (Rasheed et al., 2020 ).

As a result, importance has been attached to the task of promoting acceptance of blended learning, which is a significant concern for Chinese university students. Several studies have demonstrated the challenges faced by students, teachers, and educational institutions (Ocak, 2011 ; Broadbent, 2017 ; Medina, 2018 ; Prasad et al., 2018 ). Nevertheless, these studies have been restricted in terms of their capacity to provide a solution to this problem. Furthermore, while several previous studies have focused on the formulation and application of the blended teaching model based on the teaching cases associated with the course, empirical research on the adoption of blended learning from the standpoint of students and the Chinese scenario has been distinctly lacking. Mustafa and Garcia ( 2021 ) found that various information system (IS) theories have been integrated into the technology acceptance model (TAM) to improve our understanding of the intention to adopt online learning. Their results showed that task-technology fit (TTF) theory and the theory of planned behavior (TPB) are popular and successful theories that have been combined with the TAM. The TAM is a widely used theoretical framework in information systems and technology research, which aims to understand and predict how users adopt and accept new technologies (Davis, 1985 ). On the other hand, the TPB is a social psychological theory predicting human behavior (Ajzen, 1985 , 1991 ; Ajzen and Fishbein, 2000 ). In addition, the research on which they focused is unusual in that it employed an integrated conceptual adoption framework based on the TAM and the TPB from the perspective of Chinese undergraduates for the first time. This approach was the first systematic attempt to scientifically explore and evaluate the variables of students’ acceptance and usage of blended learning (Virani et al., 2020 ).

In the post-pandemic era, blended learning is still an efficient and flexible mode that Chinese university students can adopt because it enables them to interact more closely, engage in more abundant experiences, and improve their understanding (Blain et al., 2022 ; Müller and Wulf, 2022 ; Wu and Luo, 2022 ; Yang and Ogata, 2022 ). Blended learning can not only assist students in acquiring the habit of self-study but also shed light on creative ways to overcome problems. After interviewing 48 participants in a semi-structured manner, Fletcher et al. ( 2022 ) discovered that blended learning is an ideal tool for learners in a post-COVID future. However, Gómez et al. ( 2022 ) claimed that face-to-face interactions should be incorporated into blended learning during the post-COVID-19 era. Furthermore, Callaghan et al. ( 2022 ) noted that learners perceived their technology exposure as establishing an atmosphere that can have a long-term influence, which is a cognitive tool for learning. Blended learning is a learner-centered and integrated way for learners to study in both the pre-COVID to post-COVID eras that can be investigated through exploratory survey research. Hence, blended teaching is just as popular in the post-pandemic period as it was during the pandemic. To fill the research gap regarding blended learning in a post-pandemic era, this study expands the integrated theory of the TAM and the TPB.

Based on previous debates and in response to the demand for additional data-driven research on the motivation for blended learning (Deng et al., 2019 ), the current study aims to address the following two questions:

RQ1. To what extent can an integrated conceptual adoption framework based on the TAM and the TPB explain Chinese university students’ adoption of blended learning?

RQ2. What factors influence university students’ adoption of blended learning?

This research is intended to contribute to arguments about the adoption of blended learning. This paper intends to construct a conceptual model that explains university students’ intention to adopt blended learning (IABL) by integrating the TAM and the TPB and to validate five explanatory variables in this context, including perceived usefulness (PU), perceived ease of use (PEU), learning attitudes (LA), subjective norms (SN) and perceived behavioral control (PBC).

Literature review and research hypotheses

Blended learning.

Defined as “a judicious blending of face-to-face learning experiences in the classroom with online activities”, blended learning combines face-to-face teaching with technology-mediated teaching (Garrison and Kanuka, 2004 ; Porter et al., 2014 ). Since the beginning of the twenty-first century, educational institutions have adopted a variety of approaches that blend online instruction with traditional face-to-face instruction, an approach which is known as blended, flipped, mixed, or inverted learning.

Combining educational resources with online interaction has improved the traditional face-to-face model and the entire online form of instruction. Namely, when implemented correctly, this strategy combines the advantages of both the face-to-face and online learning modes of training (Broadbent, 2017 ; Darling-Aduana and Heinrich, 2018 ). Blended learning, for example, decreases barriers between professors and their students in online transactions and enhances interaction (Jusoff and Khodabandelou, 2009 ). Not only does it provide adaptability, educational depth, and cost-effectiveness (Graham, 2006 ), it also promotes value interaction and learning participation (Dziuban et al., 2004 ). Hence, it is beneficial for various types of students (Heinze and Procter, 2004 ).

Due to the popularity of the “Internet+” education application, the notion of blended teaching has steadily emerged (Bai et al., 2016 ; Tang et al., 2020 ; Míguez-Álvarez et al., 2020 ). Hence, blended learning has been regarded as the third generation of advancement in higher education. Traditional face-to-face education is the first generation, and e-learning education is the second generation (Park et al., 2019 ; Dang et al., 2016 ), although it is becoming more common at all educational levels. Blended learning tools can bridge the gap between traditional offline and online learning based on networks. A significant portion of the blended learning curriculum (between 30% and 80%) is delivered online (Bazelais et al., 2018 ). According to this rationale, the blended educational approach may be regarded as a contemporary technique used to facilitate teaching and learning. Some institutes use flipped classroom arrangements, which combine offline and online instruction (Kasat et al., 2019 ). This approach is a novel learning paradigm that supplements traditional course teaching with online activities (Benbunan-Fich, 2008 ). Therefore, for developing countries such as China, blended learning is an acceptable technology because of the issues they face, such as a large number of students, scarce resources, tight budgets, and limited space (Halan, 2005 ; Virani et al., 2020 ). Blended learning involves a restructuring of curriculum design that aims to activate students’ initiative in participating in online learning (Yin and Yuan, 2021 ).

According to several studies, incorporating information technology (IT) into the teaching process enhances course access and learning opportunities (Turvey and Pachler, 2020 ). Compared to traditional teaching, arousing students’ interest, fostering their enthusiasm, and inspiring their imagination and self-learning consciousness are all positive effects of blended education (Popa et al., 2020 ).

Technology acceptance model

The TAM, which was initially proposed by Davis ( 1985 ) based on the Theory of Reasoned Action (TRA) developed by Fishbein and Ajzen ( 1975 ), is a significant model used to research the variables that influence consumers’ acceptance of information system technology. Developed by Davis et al. ( 1989 ), the TAM is useful for describing and predicting user behavioral intentions regarding information systems. Venkatesh et al. ( 2003 ) claimed that behavioral intention, which has bene widely recognized as an agent of acceptance, is the most direct antecedent of technology use, according to the TAM, and is also a fully validated predictor of actual behavior (Tao et al., 2018 ). PU and PEU are two beliefs that influence behavioral intention. The extent to which an individual perceives that employing technology can boost their ability to accomplish their tasks is known as PU, and the degree to which they feel that doing so will be labor-free is known as PEU (Davis et al., 1989 ). PEU also has a significant and beneficial influence on PU. Two important primary views within the TAM, i.e., PU and PEU, were developed by synthesizing self-efficacy theory, expectation theory, etc. In addition, the TAM, which includes behavior intention, attitudes, actual usage, and external variables, can explain or predict factors that impact the use of IT (Straub et al., 1995 ).

The TAM has proven to be capable of explaining technological acceptance in various situations, including information systems for online banking (Chandio et al., 2017 ), informatics in health (Tao et al., 2018 ), apps for social networks (Chen et al., 2019 ), internet banking services (Patel and Patel, 2018 ), autonomous vehicles (Zhang et al., 2019 ), digital technology in education (Scherer et al., 2019 ), mobile tourism apps (Chen and Tsai, 2019 ), and self-driving cars (Jászberényi et al., 2022 ), etc. In MOOCs and other e-learning applications, these models have also been explored and expanded (Agudo-Peregrina et al., 2014 ; Balaman and Baş, 2021 ; Fianu et al., 2018 ; Hsu et al., 2018 ; Scherer et al., 2019 ; Šumak et al., 2011 ; Wang et al., 2020 ; Wu and Chen, 2017 ; Yoon, 2016 ). Furthermore, the TAM has been used in various studies to assess learners’ intentions to continue using e-learning systems. Chow et al. ( 2012 ) showed that when utilizing well-known online study platforms, PU and PEU can boost learning motivation. Hence, they have positive effects on learning through the platform. Similarly, Abdullah and Ward ( 2016 ), Islam ( 2013 ), Ali et al. ( 2013 ), and Zhou et al. ( 2021 ) employed the TAM to investigate the impacts of the online system of learning, indicating that PU and PEU can influence the results. Recently, Bai and Jiang ( 2022 ) found that the TAM offers influencing factors that support the use of digital resources according to a meta-analysis of 19 research articles. The TAM is thus a sound and reliable paradigm according to the data. Additionally, Alqahtani et al. ( 2022 ) utilized the TAM to investigate students’ perceptions of continuing to use online platforms following the outbreak of COVID-19. As a result, the TAM was used as a foundational theory in this study to examine the behavioral intentions associated with blended learning. However, this model focuses on the effect of perceptual traits rather than the influence of social aspects, and its explanatory capacity might be improved. Therefore, empirical research on the intention of university students to adopt blended learning using the TAM remains limited (Al-Azawei et al., 2017 ). Simultaneously, given the limits of the TAM in terms of interpretation, this study seeks to combine the TAM with the TPB to uncover the influencing mechanism underlying university students’ intention to adopt blended learning more effectively.

Theory of planned behavior

According to the theory of planned behavior (TPB) (Ajzen, 1985 , 1991 ; Ajzen and Fishbein, 2000 ), attitudes influence a particular behavior indirectly because of the relationship between SN and PBC. Given a strong desire to perform a specific task, that task is more likely to be completed. A person’s attitudes toward the activity show how much that person appreciates given conduct and whether the person anticipates that behavior will result in the associated consequences and values. The evaluation of one’s resources, abilities, and competencies with regard to the relevant action is referred to as PBC. Although behavioral intention mediates the effects of attitudes and SN on a particular behavior (Fishbein, 1979 ), Ajzen ( 1985 ) contended that the influence of PBC on behavior becomes manifest both directly and indirectly via behavioral intention (Knauder and Koschmieder, 2019 ).

On a metatheoretical level, based on the TRA of Fishbein and Ajzen ( 1975 ; 1980 ), Ajzen ( 1985 ) proposed in the TPB that individuals systematically analyze information and behave in terms of their outcomes (benefits). These outcomes have been perceived subjectively as the expectations of others who are significant to the individual in question. The TPB has been objectively validated by several academics across various investigations in contexts such as travel destination (Yuzhanin and Fisher, 2016 ), academic dishonesty (Hendy and Montargot, 2019 ), sports participation (St Quinton, 2022 ), private renting (Li et al., 2022 ), and waste sorting (Bardus and Massoud, 2022 ). Zaremohzzabieh et al. ( 2019 ) and Ashaduzzaman et al. ( 2022 ) conducted a meta-analysis to examine the applicability of the TPB. Several academics have empirically validated the TPB in studies on online learning (Hadadgar, et al., 2016 ; Chu and Chen, 2016 ; Sungur-Gül and Ateş, 2021 ).

Theories of integrated TAM and TPB

This study employs the TAM (Davis et al., 1989 ) and the TPB (Ajzen, 1991 ) to evaluate the factors influencing university students’ intentions to accept blended learning. The TAM has typically been used by academics to uncover elements impacting customers to adopt the new technology. Based on the TAM, individuals’ PU and PEU with respect to the technology influence the attitudes, intentions, and behaviors of customers (Davis et al., 1989 ). In addition, organizational issues impact the PU and PEU associated with technology (Yousafzai et al., 2007 ). Blended learning research has shown that certain factors have significant effects on learning success, i.e., PU, LA, SN, PBC, and learning behavior; however, PEU does not have such an impact (Wang et al., 2020 ).

Nevertheless, one drawback of the TAM is that it ignores the function of SN, which is similarly crucial in assessing the intentions of individuals (Venkatesh and Davis, 2000 ). Personal expectations from society regarding whether to perform a given action are referred to as SN (Hsiao and Tang, 2014 ). SN is thus one of the most essential aspects of the TPB. According to the TPB, attitudes, SN, and PBC may explain human behavior (Ajzen, 1991 ). In terms of PU and PEU, the TAM, on the other hand, highlights the attitudes variable in the TPB (Sun et al., 2013 ). Furthermore, the SN has a comparable social influence (Thompson et al., 1991 ). Therefore, acceptance tends to be a mix of technical excellence and user characteristics. With regard to indirect factors, social influences (SI) and facilitating conditions (FC) within the organization also have an impact on acceptance (Menant et al., 2021 ). Several academics have empirically supported the integration of the TAM with the TPB in various contexts, such as the use of social media for transactions (Hansen et al., 2018 ), physical activity (Tweneboah-Koduah et al., 2019 ), drone food delivery services (Choe et al., 2021 ), telecommuting during the COVID-19 outbreak (Chai et al., 2022 ), and electric vehicle purchases (Vafaei-Zadeh et al., 2022 ).

Accordingly, this study integrated the TAM with the TPB. Hence, a new research model for blended learning can be developed to illustrate how PU, PEU, LA, SN, and PBC are crucial elements that can impact learners’ intention to adopt blended learning. Figure 1 illustrates the study’s framework.

H1–H11 refer to the 11 hypotheses.

Research hypotheses

Perceived usefulness.

Defined as the extent to which students feel that blended learning may increase their study efficiency (Davis et al., 1989 ), PU is regarded as a predictor of attitudes toward behavioral intention. According to a basic model, PU has a favorable effect on behavioral intention. In addition, it frequently influences willingness as mediated by attitudes (Bazelais et al., 2018 ; Chen and Lu, 2016 ; Zhou et al., 2021 ). The role of PU in behavioral intention has previously been investigated extensively, and the results have shown that PU is a substantial predictor of behavioral intention (Chai et al., 2022 ; Gao et al., 2019 ; Jin et al., 2021 ; Kamal et al., 2020 ; Menant et al., 2021 ; Patel and Patel, 2018 ; Scherer et al., 2019 ; Sharma, 2019 ). PU is related to students’ perceptions of blended teaching’s efficacy in increasing their learning results. Teo and Dai ( 2022 ) claimed that blended learning can flexibly organize learning time, foster active learning awareness, and improve teacher-student interaction. Students adopt blended learning if its impact is significant. Instead, refusing the integrated education approach can have a negative effect. Thus, the following hypotheses are proposed:

H1: PU positively influences LA toward blended learning.

H2: PU positively influences IABL.

Perceived ease of use

The extent to which students perceive the task of engaging in blended learning to be physically and cognitively challenging is referred to as PEU. The PEU of an item indicates how well it may be comprehended or used. People prefer to utilize considerably easier items (Davis et al., 1989 ; Lazar et al., 2020 ; Sharma, 2019 ). As noted by Venkatesh et al. ( 2003 ), students are highly concerned about the complexity of the combination of online learning and offline discussion involved in the blended learning process. According to Wu and Liu ( 2013 ), one primary condition for evaluating perceived utility is PEU. Hence, the more students experience the PEU of blended learning courses, the more eager they are to participate in blended learning, and the simpler it is for them to experience the impacts of blended teaching. As a result, the following hypotheses are proposed in this study:

H3: PEU positively impacts LA toward blended learning.

H4: PEU positively impacts PU of blended learning.

H5: PEU positively impacts IABL.

Learning Attitudes

Attitudes, as a psychological process, determine whether a person likes or dislikes something (Sreen et al., 2018 ; Balaman and Baş, 2021 ). Described as “the extent of a person’s positive or negative judgment or evaluation of the action in issue” (Fishbein and Ajzen, 1975 ), a previous study highlighted the substantial empirical relationship between attitudes and willingness to continue (Ajzen, 1991 ). Subjective appraisal of participation in blended learning for students is reflected in their behavioral attitudes. Students’ readiness to accept blended learning increases if they have positive attitudes toward participation in blended teaching. In contrast, if students’ cognitive and emotional attitudes are insufficiently favorable to participate in blended learning, this situation can diminish students’ desire to accept blended teaching (Tao et al., 2022 ). The better the college student’s attitudes toward engaging in and using the blended learning model, the more that student is to accept it (Venkatesh et al., 2003 ; Wu and Liu, 2013 ). Therefore, this study proposes the following hypothesis:

H6: LA has a positive impact on IABL.

Subjective norms

The perceived social expectation regarding a certain behavior is denoted by SN (Bardus and Massoud, 2022 ; Collins et al., 2011 ; Chu and Chen, 2016 ). This notion applies to the concept that particular individuals or groups support and promote specific behaviors. (Han et al., 2020 ; Knauder and Koschmieder, 2019 ; Shalender and Sharma, 2021 ; Sungur-Gül and Ateş, 2021 ). In other words, if a large number of people who are significant to individuals do something that is beneficial for the environment, the individual in question also tend to be sufficiently sensitive or sensible to emulate this behavior (Ashaduzzaman et al., 2022 ; Cialdini et al., 1990 ; Hendy and Montargot, 2019 ). The SN associated with embracing blended learning refers to the expectations of and compliance demands made by classmates, professors, and other relevant groups when college students participate in blended learning. Therefore, university students are impacted by their peers, class norms, and professors’ expectations. They are pushed by a variety of public viewpoints and may even feel alienated if they do not follow the standards thus expressed. SN significantly impacts the desire to utilize current instructional technologies in colleges and universities according to an empirical study by Venkatesh et al. ( 2003 ) and Asare et al. ( 2016 ). Nevertheless, previous research on whether SN influences students’ readiness to adopt blended learning has not been thorough (Dakduk et al., 2018 ; Hadadgar et al., 2016 ; Prasad et al., 2018 ). Hence, the following hypotheses are proposed:

H7: SN positively impacts LA.

H8: SN positively impacts PBC.

H9: SN positively impacts IABL.

Perceived behavioral control

According to Ajzen ( 1991 ), PBC is one of the primary elements that impact the adoption of courses in blended learning. PBC relates to the degree to which students believe that they have control over their time, energy, and resources when participating in blended learning. In addition, PBC is beneficial for learners to improve their overall performance and academic accomplishment. In terms of PBC (or self-efficacy beliefs), MacFarlane and Woolfson ( 2013 ) reported specific relationships among a general sense of optimistic self-efficacy, reform implementation, and the ability to meet challenges. The intensity of PBC is primarily influenced by external facilitation circumstances and self-efficacy. First, external promotion factors mainly indicate the controllability of external conditions when students participate in blended learning, such as time and network equipment status. Oh and Yoon ( 2014 ) and Asare et al. ( 2016 ) claimed that a lack of external circumstances decreases students’ desire to take online courses. Second, self-efficacy is related to the students’ belief that they are qualified for blended learning. Students must examine the difficulty of a particular learning assignment, assess their ability’s fit with the learning task in question, and assess their competence to complete the task. Therefore, this study proposes the following hypothesis:

H10: PBC positively impacts IABL.

The mediating roles of learning attitudes and perceived usefulness

Based on the preceding discussion and analysis of the relationships among PU, PEU, LA, and IABL, the higher the degree of PU of blended learning experienced by Chinese university students, the higher their LA toward participation, which positively influences IABL. Likewise, the higher the degree of PEU of blended learning experienced by Chinese university students, the higher their PU toward participation, which positively influences IABL. Moreover, active engagement in blended learning initiatives has the potential to stimulate university students’ willingness to embrace this pedagogical method (Venkatesh et al., 2003 ; Wu and Liu, 2013 ).

LA is a mediating variable that positively influences Chinese university students’ intention to adopt blended learning. Mustafa et al. ( 2021 ) found that PU can significantly influence users’ intention to take advantage of a particular format of library resources through their positive attitudes. Jaiswal et al. ( 2021 ) concluded that changing the user’s attitudes can increase the user’s likelihood of exhibiting the intended adoption behavior and verified that people’s attitudes toward EVs mediate the positive effect of PU on their adoption intention.

In summary, the following hypotheses are proposed:

H11: LA mediates the effect of PU on IABL.

H12: PU mediates the effect of PEU on IABL.

Based on these hypotheses, this study develops a conceptual framework by integrating the TAM and the TPB to explain university students’ willingness to accept blended learning. Taking IABL as the explained variable, PU, PEU, IABL, SN, and PBC are regarded as explanatory variables (Fig. 1 ).

Research methodology

The study model underwent validation using structural equation modeling (SEM), which is a statistical method used to generate, estimate, and evaluate causal relationships. Unlike standard regression analysis, SEM can handle multiple dependent variables simultaneously as well as independent latent variables, thus facilitating the comprehensive examination and assessment of various theoretical models. Strong inferences from structural model testing, as shown in SEM treatments (Barrett, 2007 ; Kline, 2015 ), are contingent on a high sample size (i.e., at least 200 cases). Regarding the number of questionnaire samples, Loehlin ( 2004 ) discovered that the median of the paper data samples was 198 after counting 72 SEM papers. Barrett ( 2007 ) believed that the number of samples should be eight times greater than the number of model variables. However, he also noted that the built-in maximum likelihood method is generally utilized when SEM is implemented. The chi-square value becomes dramatically inflated as the sample size surpasses 500, resulting in poor model fit. As a result, scholars have generally recommended that the sample size be between 200 and 500. In addition, SEM represents a statistical technique that has been extensively employed to investigate the intricate interrelationships among multiple variables (Eksail and Afari, 2020 ). Within the scope of this study, meticulous scrutiny is directed toward the associations that exist between the observed variables and their underlying constructs, in line with the seminal work of Kline ( 2023 ). The present inquiry effectively harnesses the capabilities of SEM, as it allows for the simultaneous examination of variables while facilitating the independent estimation of the errors associated with each variable, as noted by Kline ( 2023 ). Moreover, this method facilitates the concurrent utilization of multiple indicator variables per construct, contributing to the generation of more robust inferences at the construct level when contrasted with conventional regression methodologies (Teo, 2009 ). Hence, SEM was deemed appropriate for this investigation. The research instrument and SEM are tested and reported separately in the next section.

Participants and procedure

University students drawn from various campuses across mainland China who were either enrolled at the time or had previously completed at least one blended learning course participated in the survey. They were invited to complete the surveys. In addition, the members of the project took the initiative to contact teachers responsible for blended teaching at various universities to learn more about the students’ taking courses in blended learning. These teachers were asked to distribute paper or online surveys in the classroom. The university students completed the questionnaires voluntarily and were not compensated for their participation. From February through April 2022, a total of 233 questionnaires were collected. After excluding 32 incomplete and unclear surveys, 201 valid responses remained, resulting in an effective response rate of 86%. The outliers were removed because they might have led to inaccurate statistical results, according to Hair et al. ( 2012 ). The researchers utilized convenience sampling to select participants in this study, as this method offers certain advantages such as geographical proximity, easy accessibility, availability within a specific timeframe, and voluntary participation (Etikan et al., 2016 ). Based on the descriptive statistics of the sample, male students and female students accounted for 40.8% and 59.2% of the total population, respectively. Both undergraduate and postgraduate students at universities were included in the sample to provide a comprehensive representation of the student population. A total of 77.6% of the respondents were undergraduates, while 22.4% were postgraduates. The respondents’ general information is as follows (Table 1 ).

Research instrument

The researchers created the questionnaire based on previous comparable studies (Davis et al., 1989 ; Venkatesh et al., 2003 ; Wu and Liu., 2013 ; Ajzen and Fishbein, 2000 ) since no specialized questionnaire was available in the literature to directly collect feedback from Chinese university students. These inquiries were taken from these comparable studies and adjusted as necessary for this investigation. Because the outcomes of this discovering structure were not covered by the adopted instrument, only a few inquiries were added to the investigative tool. These initiatives were further modified to suit the current study context after being validated by previous studies on the technology acceptance of blended learning (see Table 2 ). Moreover, the items were back-translated by two scholars who were fluent in Chinese and English (Brislin, 1970 ). The instrument is divided into two sections: the first section records the demographic data of the respondents (see Table 1 ), while the second section contains questions intended to gauge the constructs included in the suggested theoretical model (see Table 2 ). Six latent variables are included in the scale design: PEU, PU, LA, SN, PBC, and IABL. Responses are scored on a seven-point Likert scale (ranging from 7: totally agree to 1: totally disagree) for the observed variables of each concept. The initial questionnaire consisted of four demographic questions and 24 items pertaining to the research model. First, a pilot test was conducted, and 46 test data points were collected for analysis. Two items that did not meet the relevant standards in terms of the modification indices were deleted based on the results of the analysis. Therefore, 22 items remained and were included in the formal questionnaire (see Table 2 ). The scales used were all derived from authoritative and mature questionnaires, which can to a certain extent guarantee the face and content validity of the questionnaires used. Simultaneously, this questionnaire was examined and debated by three professors working in the field of blended learning and received their unanimous affirmation, thus further ensuring the face and content validity of the questionnaire.

Data analysis and results

SEM was used for data analysis due to its capacity to estimate numerous interconnected dependence connections based on observable and latent components while accounting for estimation errors (Hair et al., 2011 ). Fornell and Larcker ( 1981 ) provided three criteria to determine the convergent validity of the measurement model: (1) item reliability, (2) the composite reliability (CR) of each construct, and (3) the average variance extracted (AVE). Moreover, to assess item reliability, all item factor loadings were significant and greater than 0.50 (Hair et al., 2005 ). CR measures the internal consistency reliability of a latent construct. A threshold value of 0.70 or higher is often considered to be acceptable, indicating that the measures consistently represent the same latent construct (DeVellis, 2003 ). AVE quantifies the amount of variance hat is captured by a construct relative to measurement error. A threshold value of 0.50 or higher is typically considered to be adequate, suggesting that the construct explains more than 50% of the variance in its indicators (Fornell and Larcker, 1981 ). To evaluate discriminant validity, the square root of a specific construct’s AVE was compared to its correlation with all other constructs. Discriminant validity was deemed acceptable if the square root of the AVE was larger than the correlations.

The constructs should be assessed before evaluating the model fit, thus verifying the reliability and validity of the questionnaire used in the study. The test is evaluated using four indicators: Cronbach’s alpha, CR, AVE, and Variance Inflation Factor (VIF). In a confirmatory investigation guided by mature theories, Cronbach’s alpha should be greater than 0.8, CR larger than 0.7, AVE more than 0.5, and VIF close to 3 or lower (Hair et al., 2009 ). In this study, the values of these indicators were calculated using SPSS 27.0 and AMOS 26.0 software (developed by IBM in Armonk), revealing that Cronbach’s alpha was more than 0.8, CR was greater than 0.7, AVE was higher than 0.5, and VIF was lower than 3 (see Table 3 ). According to the results shown in Table 3 , these claims held for all six constructs, suggesting that the proposed model satisfies the convergent validity criteria.

The validity test is used to assess discriminant validity between variables. A low correlation and a substantial difference between two latent variables are known as discriminant validity. Discriminant validity may be evaluated by comparing the square root of AVE to the correlation coefficient between variables. According to Fornell and Larcker ( 1981 ), a variable has high discriminant validity if the correlation coefficient between it and other variables is less than the square root of the AVE of the variable. The data shown in bold font in the table are the square root of the AVE, which is larger than all the values included in the table in which it is located, as shown in Table 4 . Hence, the discriminant validity of the measurement model included in this study is acceptable and suitable.

Because SEM lacks a separate and powerful evaluation index such as traditional analysis techniques such as ANOVA and regression, it is frequently necessary to compare the covariance matrix of the sample to that of the theoretical model to evaluate its fit effect. AMOS software allows for 25 different types of fit indices, but not all of these indices must be reported. The most frequently reported indicators that measure the fitness of structural equation models are shown in Table 5 . The aforementioned fit indices offer diverse viewpoints regarding the adequacy of the SEM, taking into account multiple dimensions of the model’s intricacy and efficacy. Obtaining a comprehensive assessment of the model fit is often achieved by reporting multiple fit indices.

No fixed standard for fit indices has been universally established. While minimizing the chi-square value is desirable, the influence of sample size expansion on the chi-square value can affect its reference value. Hair et al. ( 2009 ) highlighted the potential significance of the chi-square statistic as sample size increases. With respect to larger sample sizes, even minor deviations in the model can be magnified, leading to a statistically significant chi-square value. However, it is important to note that the significance of the chi-square statistic does not necessarily imply poor model fit. A significant chi-square value should not be automatically interpreted as indicating a lack of adequacy in the model (Hair et al., 2009 ).

Consequently, various indicators based on chi-square values have been developed, and the specific research context also influences the choice of fit index standards. For example, standards for fit indices differ between confirmatory and exploratory research, with exploratory research often employing lower standards than confirmatory research. Furthermore, variations in established standards exist. Therefore, researchers often consult the suggestions of authoritative scholars in the field of structural equations when evaluating model fit (Hayduk, 1987 ; Bagozzi and Yi, 1988 ; Hu and Bentler, 1998 ; Hair Jr et al., 2017 ). Table 5 presents the numerical findings and recommended values for the indices of the proposed model in this study. The goodness-of-fit indices meet the necessary threshold, indicating that the model is well-suited to the provided data based on comparative analysis.

IBM AMOS 26 software was utilized in this research to effectively analyze the data and test the hypotheses within the framework of SEM, thereby enhancing the rigor and accuracy of the study’s findings. In this study, SEM analysis was employed for estimates, as was the validation of the conceptual framework by reference to statistical results and its links to outlier results (Shah and Goldstein, 2006 ). Figure 2 depicts the model after testing all six hypotheses collectively. The regression weights are indicated by the arrows. Table 6 summarizes the hypotheses.

Values on the straight arrows between variables represent the standardised path coefficients.

According to Fig. 2 and Table 6 , the following seven of the ten relationships indicated in the study framework were validated: PU → LA, PEU → LA, SN → LA, PEU → PU, SN → PBC, PU → IABL, and LA → IABL. PU ( β  = 0.301, p  < 0.001), PEU ( β  = 0.291, p  < 0.05), and SN ( β  = 0.324, p  < 0.001) have a positive influence on LA toward blended learning, which explains 62% of the variation. This finding indicates that 62% of the variation in learners’ attitudes variables could be explained by the three independent variables, i.e., PU, PEU, and SN. In addition, PEU ( β  = 0.647, p  < 0.001) has a substantial impact on PU, collectively contributing 41.8% of the variance. Moreover, SN ( β  = 0.711, p  < 0.001) significantly affects PBC, accounting for 50.6% of the variation. Among the factors affecting IABL, only two variables, i.e., PU ( β  = 0.229, p  < 0.01) and LA ( β  = 0.226, p  < 0.05), were significant, explaining 67.6% of the variance of the IABL variable; in contrast, the other three variables, i.e., PEU, SN, and PBC, were not significant.

In conclusion, the study found that PU, PEU, and SN had significant impacts on LA. SN had the greatest impact on LA, with a path coefficient of 0.324. This result indicated that higher levels of subjective norms were associated with higher levels of learning attitudes among Chinese university students. PU and PEU had the second and the third greatest impacts on LA, with path coefficients of 0.301 and 0.291, respectively, which also affected LA toward blended learning to a large extent. According to the data presented above, LA toward blended learning was more influenced by the people around the participants (or the environment in which they were located) in the post-epidemic situation. LA was also affected by this factor and became more positive. In addition, if it was more effective and convenient to engage in blended learning, this situation also improved LA toward blended learning as well as its acceptance. It can thus be concluded that the significance of PU, PEU, and SN with regard to determining LA was also proven in this investigation, echoing the findings of previous technology acceptance research (e.g., Davis et al., 1989 ; Venkatesh and Bala, 2008 ). When the present study’s findings were compared to the data reported by previous studies, it was discovered that the effects of PU and PEU on LA are compatible with the propositions of Lee ( 2010 ). The relationship between SN and LA was also validated. This finding revealed that Chinese university students’ perceptions of social expectations and pressures associated with BL were linked to their overall attitudes toward this learning approach. In other words, when Chinese university students perceive that their peers, instructors, or other influential individuals support or encourage blended learning, these perceptions might positively impact their attitudes and openness toward adopting this approach. This finding highlights the importance of SN in shaping Chinese university students’ LA and acceptance of blended learning. This result suggests that creating a positive social environment that promotes and supports blended learning could have a favorable impact on Chinese university students’ LA and willingness to engage with this learning method.

Moreover, the AMOS analysis indicated that PU and LA had a significant influence on IABL, accounting for 67.6% of the variance in IABL. In contrast, PEU, SN, and PBC had no significant impacts on IABL. The different effects of PU and PEU on IABL can be explained by reference to the two-factor theory proposed by Herzberg, an American behavioral scientist. Monitoring hygiene and motivation variables as per Herzberg’s two-factor theory is a common approach used to determine the elements that drive satisfaction and motivation. In essence, this theory recognizes two sorts of factors: hygiene factors that contribute to student dissatisfaction and motivation factors that contribute to student satisfaction (Herzberg et al. 1959 ). Hygiene factors are critical in preventing dissatisfaction, while motivation factors are essential for promoting actual satisfaction (Herzberg, 1966 ). The former term refers to factors that are dispensable when they exist but cause user dissatisfaction if they do not exist, whereas the latter are factors that contribute to user satisfaction. According to the data collection and analysis, IABL is also driven by two elements, in which context PEU represents a hygiene factor and PU serves as a motivation factor. The PU of blended learning itself is widely considered by students to fall under the general needs of learners with regard to improving their learning, while PEU is a secondary factor. These findings are also compatible with those reported by Oh and Yoon ( 2014 ), indicating that university students may be more familiar with the operation of technology and have high learning capacity because they grew up in the Internet age. According to previous research, PEU, SN, and PBC have little effect on whether learners have IABL, but PU has a more significant impact in this context (Lee, 2010 ; Chen et al., 2012 ). The findings agree with those reported by Dakduk et al. ( 2018 ) and Prasad et al. ( 2018 ), demonstrating that persuasion and the public opinions of their peers, friends, instructors, or others have no impacts on Chinese university students’ involvement in blended learning, which is rather a logical choice on their part. The reasons for this situation are as follows. One possible explanation is that learners prioritize the perceived benefits and advantages of blended learning as superior to other factors. PU is rooted in the TAM and suggests that individuals are more inclined to adopt a technology if they perceive it to be useful with regard to achieving their goals or fulfilling their needs. In the context of blended learning, learners may be motivated by potential benefits such as improved learning outcomes, enhanced access to resources, flexibility in learning, or increased engagement. Thus, when learners perceive blended learning as useful, they are more likely to develop an intention to adopt it. Additionally, this finding may be attributed to the evolving nature of technology and its integration into education. As blended learning continues to gain increasing recognition and prominence, learners might already possess a certain degree of familiarity and comfort with the use of digital tools and platforms. Therefore, the perceived ease of use of blended learning technologies may not be as influential in their decision-making process, as learners may have already overcome initial the usability challenges through their previous experiences with technology in education. Moreover, subjective norms and perceived behavior control might have limited influence due to the complex and individualistic nature of learners' decision-making processes. Because university students are mentally mature, they are conscious of the influence of blended learning on their cognitive thinking capacity and emotional attitudes. The IABL of Chinese university students may be more driven by personal motivations, learning preferences, and perceived self-efficacy than by external social pressures or perceived control over their behavior. Learners’ perceptions of the usefulness of such learning may align more closely with their personal goals and motivations, making this factor a stronger predictor of their intention to adopt blended learning. The present blended learning strategy, on the other hand, is based on top-down promotion, which does not take into account students’ subjective acceptance of the model and is more closely related to school affairs and the faculty. It is important to note that this finding is context-specific to the adoption of blended learning and may not necessarily apply to other educational contexts or technology adoption scenarios. However, from an academic standpoint, this finding provides insights into the factors that influence learners’ IABL and highlights the significance of emphasizing the PU of blended learning when promoting its adoption by Chinese university students.

Finally, to test for the existence of a mediating effect, we performed percentile bootstrapping and bias-corrected percentile bootstrapping (Taylor et al., 2008 ) on 5000 bootstrapped samples with 95% confidence intervals, examining PU, LA and PBC for three mediating variables. We followed the suggestion of Preacher and Hayes ( 2008 ) and calculated the confidence interval of the upper and lower bounds to test whether the indirect effect was significant. Significant summative effects were found in all paths studied. As shown in Table 7 , the results of the bootstrap test confirmed the positive and significant mediating effect of LA in the relationship between PU and IABL (standardized indirect effect of 0.299, p  < 0.001), and PU played a significant mediating role in the relationship between PEU and IABL (the standardized indirect effect was 0.267, p  < 0.001). Therefore, H11 and H12 were supported. PU had a significant indirect positive effect on the IABL of Chinese university students through LA. PU improved the effect of attitudes on IABL. Simultaneously, PEU had a significant indirect positive effect on the IABL of college students through PU. For Chinese university students, more attention is given to the practical utility of blended learning, while less attention is given to the difficulty of participating in blended teaching. If students have strong perceptions of the actual effect of blended learning, these perceptions can promote their positive attitudes toward participating in blended teaching and thus enhance their willingness to accept blended teaching. Similarly, if students perceive the ease of participating in blended teaching more strongly, the effect of improving students’ positive attitudes and willingness to participate in blended teaching is slightly weaker. To effectively implement the blended teaching model, it is essential to prioritize its usability while emphasizing student-centered approaches. By enhancing university students’ learning experiences, the blended teaching model can demonstrate its remarkable practical value, thereby fostering increased acceptance of blended learning among a wider student population. Therefore, the tasks of optimizing student outcomes and creating an engaging learning environment should be a key focus, which can subsequently bolster students’ willingness to embrace blended learning.

Implications

This research has important implications for future studies on how intention indicators may encourage learners to adopt blended learning.

Theoretical implications

As described in section 2, the theoretical grounds for this work were Davis’s ( 1985 ) TAM and the TPB (Ajzen, 1985 ). The TAM was developed as a critical theory to justify and predict user acceptance of technology because it conceptualizes the elements that impact consumers’ adoption of information system technology. Individuals’ attitudes toward technology may explain their usage of technology according to the TPB. The current study contributes to this theoretical framework through an empirical analysis of the questionnaire findings to offer further evidence on blended learning. Similarly, by continuing research into blended learning in social studies classrooms at Chinese colleges, this study scientifically enhances scholarly understanding of this topic. This study is not only theoretically significant but also contributes to the corpus of scholarly and practitioner-based information concerning blended learning among university students.

The TAM and the TPB have been widely employed in technology acceptance research. However, these models have received criticism due to their perceived limitations, which have hindered their ability to effectively explain technology acceptance in academic contexts. This critique stems from their restrictive nature, which has led to an inadequate understanding of the complex dynamics underlying technology acceptance within educational environments. Notably, the close relationship between user-technology interaction and the educational setting highlights the need for a more comprehensive approach (Al-Emran et al., 2018 ). To address the limitations of the TAM and the TPB, researchers have increasingly embraced the integration of these two models. This integration aims to overcome the oversimplification inherent in these models and to develop a more comprehensive framework. The theoretical underpinnings of this research highlight ongoing discussions regarding the suitability of the TAM in educational contexts. The proposed model seeks to establish a cohesive framework by combining the TAM and the TPB to examine the factors influencing the adoption of blended learning in educational environments.

The findings of this study demonstrate that the research methodology employed in this context effectively supports the primary objective of the study. Specifically, the integrated model offers a robust theoretical foundation for explaining the adoption of blended learning. Furthermore, future studies are encouraged to explore the incorporation of additional theories alongside the TAM and the TPB. The Task-Technology Fit (TTF) Theory, the Unified Theory of Acceptance and Use of Technology (UTAUT), the Theory of Self-Regulation (TSR), and the Expectation Confirmation Model (ECM) are suggested as potential theories that can be referenced in future investigations. By incorporating these complementary theories, researchers can gain a more comprehensive understanding of the multifaceted factors that influence technology acceptance in educational contexts. This integrative approach can contribute to advancing knowledge in this field and provide valuable insights into practical applications.

Practical implications

To improve university students’ attitudes toward blended learning and their perceived behavioral control, it is essential to focus on their actual needs. University students are autonomous and independent learners who prioritize their academic performance and cognitive development. Meeting university students’ actual needs requires a combination of course content characteristics and learners’ cognitive development. This approach not only allows knowledge to be transmitted and resources to be provided but also facilitates the cultivation of university students’ innovative and autonomous learning abilities. Hence, it is crucial to promote the blended learning support system, including teaching content, methods, and platform design.

Blended learning places university students at the center and emphasizes the development of their autonomous learning abilities. The effectiveness of online teaching is largely dependent on the design of an online teaching platform with good functions, a user-friendly interface, and abundant resources. Teachers play an important role in this process by summarizing online learning content, providing guidance and motivation, and inspiring university students’ thinking through interaction, discussion, and case analysis, thereby enhancing the interaction and synergy between online and offline learning. Furthermore, the effective implementation of blended learning requires the support of university management in terms of infrastructure, teaching staff, technical personnel, and student preparation. Management support is crucial for implementing blended teaching, and university management staff should play a vital role in providing instructional management and both psychological and emotional support for students. Additionally, it is important to enhance university students’ self-efficacy and development of subjective consciousness. Blended learning combines various modes of learning, such as online, space-time, open, real-time, and community learning. To adjust to blended learning, university students require encouragement and guidance to boost their confidence and initiative with regard to participating in the course. Finally, student-oriented pedagogy should be advocated throughout the learning process, thereby encouraging university students to adopt proactive attitudes toward blended learning.

Limitations and directions for future research

Although this research performed substantial work to investigate the issue in question, the writers did not discuss this topic exhaustively. The study might thus have some drawbacks. First and foremost, because the study was performed in a metropolis, i.e., Guangzhou, the results are not generalizable. This type of quantitative research was performed through a small-scale, monocultural study based on a higher education context in China. University student samples are drawn from distinct contexts and groups. To evaluate the validity of these conclusions more effectively, future scholars are urged and encouraged to employ additional comparable study designs to investigate diverse samples of college students from varied academic backgrounds, such as different university backgrounds, various class sizes, and diverse professional programs. Additionally, because of the cross-sectional nature of the study (i.e., the data collected for the hypotheses were confirmed by distributing questionnaires at a particular moment in time), we could not completely comprehend the underlying dynamics from university students’ perspectives. To overcome this limitation, future studies should employ a longitudinal approach to acquire a more thorough understanding of the dynamics associated with the variables over time. Furthermore, exploring other variables that may influence teacher acceptance of blended learning would be interesting. A qualitative study might be performed in pursuit of the same goal. As proposed by Arora and Saini ( 2013 ), probabilistic neural networks can be used to predict students’ learning achievements in blended learning. The present study focused on students’ opinions, but the perceptions of instructors and the administrative bodies of higher education institutions can also be considered to represent prospective research directions.

Against the backdrop of existing research, the main contributions and innovations of the study lie in its exploration of students’ adoption of blended learning based on the TAM and the TPB from the perspective of Chinese undergraduates for the first time, thereby expanding the integrated theory of the TAM and the TPB. In addition, this paper contributes by providing a deeper knowledge of blended learning, particularly by identifying the path that improves its learning impact on university students in an innovative academic environment. Based on the TAM and TPB perspectives, data were acquired by administering a questionnaire that was completed by university students. According to a statistical study, the paths by which blended learning performance can be improved are that PU, PEU, and SN influence LA and PU affects PEU. Only PU and LA affect IABL. PEU, SN, and PBC, on the other hand, do not affect IABL. Based on these findings, four recommendations for enhancing higher education delivery through blended learning are provided. First, it is necessary to improve the curriculum’s practicability and implement differentiated instruction. Moreover, in cases of limited resources, the faculty is advised to take advantage of the features of Chinese university students to teach them in a manner that depends on both their ability and their differences in various aspects of learning. Furthermore, teachers should focus on PU when designing courses to improve learning efficiency and the effectiveness of blended learning. As a result, a process of promoting blended learning awareness that extends from top administrators to learners should be implemented. Universities can create the majority of the materials and platforms needed to raise awareness of blended learning among teachers and students. Finally, it is essential to promote online functions and expand communication and engagement channels. Effective learning initiatives can influence learners’ attitudes and behaviors; thus, teachers must remain up to date on current topics to ensure that they can adjust their teaching approaches to fulfill learners’ needs in the future.

Data availability

The datasets analyzed during the current study are available in the Dataverse repository: https://doi.org/10.7910/DVN/PBJ2GY .

Abdullah F, Ward R (2016) Developing a general extended technology acceptance model for e-learning (GETAMEL) by analysing commonly used external factors. Comput Hum Behav 56:238–256. https://doi.org/10.1016/j.chb.2015.11.036

Article   Google Scholar  

Agudo-Peregrina ÁF, Hernández-García Á, Pascual-Miguel FJ (2014) Behavioral intention, use behavior and the acceptance of electronic learning systems: differences between higher education and lifelong learning. Comput Hum Behav 34:301–314. https://doi.org/10.1016/j.chb.2013.10.035

Ajzen I (1985) From intentions to actions: a theory of planned behavior. In: Kuhl J, Beckmann J (eds.) Action control: from cognition to behavior. Springer, Berlin, pp. 11–39

Ajzen I (1991) The theory of planned behavior. Organ Behav Hum Decis Process 50(2):179–211. https://doi.org/10.1016/0749-5978(91)90020-T

Ajzen I, Fishbein M (1980) Understanding attitudes and predicting social behavior. Prentice Hall, Upper Saddle River

Google Scholar  

Ajzen I, Fishbein M (2000) Attitudes and the attitude-behavior relation: reasoned and automatic processes. Eur Rev Soc Psychol 11(1):1–33. https://doi.org/10.1080/14792779943000116

Al-Azawei A, Parslow P, Lundqvist K (2017) Investigating the effect of learning styles in a blended e-learning system: an extension of the technology acceptance model (TAM). Australas J Educ Technol 33(2):1–23. https://doi.org/10.14742/ajet.2741

Al-Emran M, Mezhuyev V, Kamaludin A (2018) Technology acceptance model in m-learning context: a systematic review. Comput Educ 125:389–412. https://doi.org/10.1016/j.compedu.2018.06.008

Ali L, Asadi M, Gašević D, Jovanović J, Hatala M (2013) Factors influencing beliefs for adoption of a learning analytics tool: an empirical study. Comput Educ 62:130–148. https://doi.org/10.1016/J.COMPEDU.2012.10.023

Alqahtani MA, Alamri MM, Sayaf AM, Al-Rahmi WM (2022) Investigating students’ perceptions of online learning use as a digital tool for educational sustainability during the COVID-19 pandemic. Front Psychol 13:886272. https://doi.org/10.3389/fpsyg.2022.886272

Article   PubMed   PubMed Central   Google Scholar  

Arora N, Saini JR (2013) A fuzzy probabilistic neural network for student’s academic performance prediction. Int J Innov Res Sci Eng Technol 2(9):4425–4432

Asare AO, Yun-Fei SHAO, Adjei-Budu K (2016) Adoption of e-learning in higher education: expansion of UTAUT model. Eur Acad Res 3(12):13236–13259. https://doi.org/10.1111/j.1467-8535.2005.00482.x

Ashaduzzaman M, Jebarajakirthy C, Weaven SK, Maseeh HI, Das M, Pentecost R (2022) Predicting collaborative consumption behaviour: a meta-analytic path analysis on the theory of planned behaviour. Eur J Market 56:968–1013. https://doi.org/10.1108/EJM-07-2020-0563

Bagozzi RP, Yi Y (1988) On the evaluation of structural equation models. J Acad Mark Sci 16(1):74–94. https://doi.org/10.1177/009207038801600107

Bai Y, Mo D, Zhang L, Boswell M, Rozelle S (2016) The impact of integrating ICT with teaching: evidence from a randomized controlled trial in rural schools in China. Comput Educ 96:1–14. https://doi.org/10.1016/j.compedu.2016.02.005

Bai YQ, Jiang JW (2022) Meta-analysis of factors affecting the use of digital learning resources. Interact Learn Environ 30:1–12. https://doi.org/10.1080/10494820.2022.2091608

Balaman F, Baş M (2021) Perception of using e-learning platforms in the scope of the technology acceptance model (TAM): a scale development study. Interact Learn Environ 29:1–25. https://doi.org/10.1080/10494820.2021.2007136

Bardus M, Massoud MA (2022) Predicting the intention to sort waste at home in rural communities in Lebanon: an application of the theory of planned behaviour. Int J Environ Res Public Health 19(15):9383. https://doi.org/10.3390/ijerph19159383

Barrett P (2007) Structural equation modelling: adjudging model fit. Pers Individ Differ 42(5):815–824. https://doi.org/10.1016/j.paid.2006.09.018

Bazelais P, Doleck T, Lemay DJ (2018) Investigating the predictive power of TAM: a case study of CEGEP students’ intentions to use online learning technologies. Educ Inf Technol 23(1):93–111. https://doi.org/10.1007/s10639-017-9587-0

Benbunan-Fich R (2008) Review of blended learning in higher education: framework, principles, and guidelines, by D. R. Garrison & N. D. Vaughan. Acad Manag Learn Educ 7(1):135–137. https://doi.org/10.5465/AMLE.7.1.31413871B

Blain DO, Standage M, Curran T (2022) Physical education in a post-COVID world: a blended-gamified approach. Eur Phys Educ Rev 28(3):757–776. https://doi.org/10.1177/1356336X221080372

Brislin RW (1970) Back-translation for cross-cultural research. J Cross-Cult Psychol 1(3):185–216. https://doi.org/10.1177/135910457000100301

Broadbent J (2017) Comparing online and blended learner’s self-regulated learning strategies and academic performance. Internet High Educ 33:24–32. https://doi.org/10.1016/j.iheduc.2017.01.004

Cahapay MB (2020) A reconceptualization of learning space as schools reopen amid and after COVID-19 pandemic. Asian J Distance Educ 15(1):269–276. https://doi.org/10.5281/ZENODO.3892969

Callaghan R, Joubert J, Engelbrecht J (2022) Using enaction to evolve from pre-Covid to post-Covid pedagogy: a case study with South African mathematics teachers. ZDM-Math Educ 54:1–14. https://doi.org/10.1007/s11858-022-01416-9

Chai L, Xu J, Li S (2022) Investigating the intention to adopt telecommuting during COVID-19 outbreak: an integration of TAM and TPB with risk perception. Int J Hum–Comput Interact 26:1–11. https://doi.org/10.1080/10447318.2022.2098906

Chandio FH, Irani Z, Zeki AM, Shah A, Shah SC (2017) Online banking information systems acceptance: an empirical examination of system characteristics and web security. Inf Syst Manage 34(1):50–64. https://doi.org/10.1080/10580530.2017.1254450

Chen CC, Tsai JL (2019) Determinants of behavioral intention to use the Personalized Location-based Mobile Tourism Application: an empirical study by integrating TAM with ISSM. Futur Gener Comp Syst 96:628–638. https://doi.org/10.1016/J.FUTURE.2017.02.028

Chen SC, Yen DC, Hwang MI (2012) Factors influencing the continuance intention to the usage of Web 2.0: an empirical study. Comput Hum Behav 28(3):933–941. https://doi.org/10.1016/j.chb.2011.12.014

Chen SY, Lu CC (2016) A model of green acceptance and intentions to use bike-sharing: YouBike users in Taiwan. Netw Spat Econ 16(4):1103–1124. https://doi.org/10.1007/s11067-015-9312-8

Article   MathSciNet   MATH   Google Scholar  

Chen X, Tao D, Zhou Z (2019) Factors affecting reposting behaviour using a mobile phone-based user-generated-content online community application among Chinese young adults. Behav Inf Technol 38(2):120–131. https://doi.org/10.1080/0144929X.2018.1515985

Cheng P, OuYang Z, Liu Y (2019) Understanding bike sharing use over time by employing extended technology continuance theory. Transp Res Pt A-Policy Pract 124:433–443. https://doi.org/10.1016/j.tra.2019.04.013

Choe JY, Kim JJ, Hwang J (2021) Innovative marketing strategies for the successful construction of drone food delivery services: merging TAM with TPB. J Travel Tour Mark 38(1):16–30. https://doi.org/10.1080/10548408.2020.1862023

Chow M, Herold DK, Choo TM, Chan K (2012) Extending the technology acceptance model to explore the intention to use second life for enhancing healthcare education. Comput Educ 59:1136–1144. https://doi.org/10.1016/j.compedu.2012.05.011

Chu TH, Chen YY (2016) With good we become good: understanding e-learning adoption by theory of planned behavior and group influences. Comput Educ 92:37–52. https://doi.org/10.1016/j.compedu.2015.09.013

Cialdini RB, Reno RR, Kallgren CA (1990) A focus theory of normative conduct: recycling the concept of norms to reduce littering in public places. J Pers Soc Psychol 58(6):1015–1026. https://doi.org/10.1037/0022-3514.58.6.1015

Collins SE, Witkiewitz K, Larimer ME (2011) The theory of planned behavior as a predictor of growth in risky college drinking. J Stud Alcohol Drugs 72(2):322–332. https://doi.org/10.15288/jsad.2011.72.322

Dakduk S, Santalla-Banderali Z, van der Woude D (2018) Acceptance of blended learning in executive education. SAGE Open 8(3):1–16. https://doi.org/10.1177/2158244018800647

Dang YM, Zhang YG, Ravindran S, Osmonbekov T (2016) Examining student satisfaction and gender differences in technology-supported, blended learning. J Inf Syst Educ 27(2):119–130. https://jise.org/Volume27/n2/JISEv27n2p119.html

Darling-Aduana J, Heinrich CJ (2018) The role of teacher capacity and instructional practice in the integration of educational technology for emergent bilingual students. Comput Educ 126:417–432. https://doi.org/10.1016/j.compedu.2018.08.002

Davis FD (1985) A technology acceptance model for empirically testing new end-user information systems: theory and results. Dissertation, Massachusetts Institute of Technology

Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manage Sci 35(8):982–1003. https://doi.org/10.1287/MNSC.35.8.982

Deng R, Benckendorff P, Gannaway D (2019) Progress and new directions for teaching and learning in MOOCs. Comput Educ 129:48–60. https://doi.org/10.1016/j.compedu.2018.10.019

DeVellis RF (2003) Scale development: theory and applications. SAGE, London

Dziuban C, Graham CR, Moskal PD, Norberg A, Sicilia N (2018) Blended learning: the new normal and emerging technologies. Int J Educ Technol High Educ 15(1):1–16. https://doi.org/10.1186/s41239-017-0087-5

Dziuban CD, Moskal P, Hartman J (2004) Higher education, blended learning, and the generations: knowledge is power no more. In: Bourne J, Moore JC (eds.) Elements of quality online education: engaging communities. Sloan Consortium, Newport, pp. 85–100

Eksail FAA, Afari E (2020) Factors affecting trainee teachers’ intention to use technology: a structural equation modeling approach. Educ Inf Technol 25(4):2681–2697. https://doi.org/10.1007/s10639-019-10086-2

Etikan I, Musa SA, Alkassim RS (2016) Comparison of convenience sampling and purposive sampling. Amer J Theor Appl Stat 5(1):1–4. https://doi.org/10.11648/j.ajtas.20160501.11

Fianu E, Blewett C, Ampong GOA, Ofori KS (2018) Factors affecting MOOC usage by students in selected Ghanaian universities. Educ Sci 8(2):70. https://doi.org/10.3390/educsci8020070

Fishbein M (1979) A theory of reasoned action: some applications and implications. Nebr Symp Motiv 27:65–116

Fishbein M, Ajzen I (1975) Belief, attitude, intention and behavior: an introduction to theory and research. Addison-Wesley, Berkshire

Fletcher J, Klopsch B, Everatt J, Sliwka A (2022) Preparing student teachers post-pandemic: lessons learnt from principals and teachers in New Zealand and Germany. Educ Rev 74(3):609–629. https://doi.org/10.1080/00131911.2021.2007053

Fornell C, Larcker DF (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mark Res 18(1):39–50. https://doi.org/10.1177/002224378101800104

Gao S, Li Y, Guo H (2019) Understanding the adoption of bike sharing systems: by combining technology diffusion theories and perceived risk. J Hosp Tour Technol 10(3):494–508. https://doi.org/10.1108/JHTT-08-2018-0089

Garrison DR, Kanuka H (2004) Blended learning: uncovering its transformative potential in higher education. Internet High Educ 7(2):95–105. https://doi.org/10.1016/j.iheduc.2004.02.001

Gómez CJ, Hinojo-Lucena FJ, Moreno-Vera JR, Alonso-Garcia S (2022) Analysis of a forced blended-learning program in social sciences higher education during the COVID-19 post-pandemic. Educ Train. https://doi.org/10.1108/ET-06-2022-0246

Graham RG (2006) Definition, current trends, and future directions. In: Bonk CJ, Graham CR (eds.) The handbook of blended learning: global perspectives, local designs. Pfeiffer Publishing, San Francisco, pp. 3–21

Hadadgar A, Changiz T, Masiello I, Dehghani Z, Mirshahzadeh N, Zary N (2016) Applicability of the theory of planned behavior in explaining the general practitioners eLearning use in continuing medical education. BMC Med Educ 16(1):1–8. https://doi.org/10.1186/s12909-016-0738-6

Hair JF, Black WC, Babin BJ, Anderson RE (2009) Multivariate data analysis (7th ed.) Prentice Hall, Upper Saddle River

Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL (2005) Multivariate data analysis (6th ed.) Prentice Hall, Upper Saddle River

Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Market Theory Pract 19(2):139–152. https://doi.org/10.2753/MTP1069-6679190202

Hair JF, Ringle CM, Sarstedt M (2012) Partial least squares: the better approach to structural equation modeling? Long Range Plan 45(5–6):312–319. https://doi.org/10.1016/j.lrp.2012.09.011

Hair Jr JF, Babin BJ, Krey N (2017) Covariance-based structural equation modeling in the Journal of Advertising: review and recommendations. J Advert 46(1):163–177. https://doi.org/10.1080/00913367.2017.1281777

Halan D (2005) Blended learning. I-manager’s J Educ Technol 2(1):20–24

Han H, Chua BL, Hyun SS (2020) Consumers’ intention to adopt eco-friendly electric airplanes: the moderating role of perceived uncertainty of outcomes and attachment to eco-friendly products. Int J Sustain Transp 14(9):671–685. https://doi.org/10.1080/15568318.2019.1607957

Hansen JM, Saridakis G, Benson V (2018) Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Comput Hum Behav 80:197–206. https://doi.org/10.1016/j.chb.2017.11.010

Hayduk LA (1987) Structural equation modeling with LISREL: essentials and advances. Jhu Press, Washington

Heinze A, Procter CT (2004) Reflections on the use of blended learning. In: Proceedings of education in a changing environment, University of Salford, Salford, 13–14 Sept 2004

Hendy NT, Montargot N (2019) Understanding academic dishonesty among business school students in France using the theory of planned behavior. Internat J Manag Educ 17(1):85–93. https://doi.org/10.1016/j.ijme.2018.12.003

Herzberg F (1966) Work and the nature of man. World Publishing, Cleveland

Herzberg F, Mausner B, Snyderman BB (1959) The motivation to work. John Wiley & Sons, Hoboken

Hsiao CH, Tang KY (2014) Explaining undergraduates’ behavior intention of e-textbook adoption: empirical assessment of five theoretical models. Libr Hi Tech 32(1):139–163. https://doi.org/10.1108/LHT-09-2013-0126

Hsu JY, Chen CC, Ting PF (2018) Understanding MOOC continuance: an empirical examination of social support theory. Interact Learn Environ 26(8):1100–1118. https://doi.org/10.1080/10494820.2018.1446990

Hu LT, Bentler PM (1998) Fit indices in covariance structure modeling: sensitivity to underparameterized model misspecification. Psychol Methods 3(4):424–453. https://doi.org/10.1037/1082-989X.3.4.424

Islam AN (2013) Investigating e-learning system usage outcomes in the university context. Comput Educ 69:387–399. https://doi.org/10.1016/j.compedu.2013.07.037

Jaiswal D, Kaushal V, Kant R, Singh PK (2021) Consumer adoption intention for electric vehicles: insights and evidence from Indian sustainable transportation. Technol Forecast Soc Chang 173:121089. https://doi.org/10.1016/j.techfore.2021.121089

Jászberényi M, Miskolczi M, Munkácsy A, Földes D (2022) What drives tourists to adopt self-driving cars? Transp Res Pt F-Traffic Psychol Behav 89:407–422. https://doi.org/10.1016/j.trf.2022.07.013

Jin YQ, Lin C-L, Zhao Q, Yu S-W, Su Y-S (2021) A study on traditional teaching method transferring to E-learning under the COVID-19 pandemic: from Chinese students’ perspectives. Front Psychol 12:632787. https://doi.org/10.3389/fpsyg.2021.632787

Jusoff K, Khodabandelou R (2009) Preliminary study on the role of social presence in blended learning environment in higher education. Int Educ Stud 2(4):79–83. https://doi.org/10.5539/ies.v2n4p79

Kamal SA, Shafiq M, Kakria P (2020) Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM). Technol Soc 60:101212. https://doi.org/10.1016/j.techsoc.2019.101212

Kang K, Wang T, Chen S, Yu SW (2021) Push-pull-mooring analysis of massive open online courses and college students during the COVID-19 pandemic. Front Psychol 12:755137. https://doi.org/10.3389/fpsyg.2021.755137

Kasat K, Shaikh N, Chandrachud M, Saini JR (2019) Impact of flipped classroom on engagement of post-graduate students under the faculty of social sciences. In: Gómez Chova L, López Martínez A, Candel Torres I (eds.) Proceedings of the 12th annual international conference of education, research and innovation, Sevilla, 2019

Kim B (2010) An empirical investigation of mobile data service continuance: incorporating the theory of planned behavior into the expectation–confirmation model. Expert Syst Appl 37(10):7033–7039. https://doi.org/10.1016/j.eswa.2010.03.015

Kline RB (2015) Principles and practice of structural equation modeling (4th ed.). Guilford Publications, New York

Kline RB (2023) Principles and practice of structural equation modeling (5th ed.). Guilford Publications, New York

Knauder H, Koschmieder C (2019) Individualized student support in primary school teaching: a review of influencing factors using the Theory of Planned Behavior (TPB). Teach Teach Educ 77:66–76. https://doi.org/10.1016/j.tate.2018.09.012

Lazar IM, Panisoara G, Panisoara IO (2020) Digital technology adoption scale in the blended learning context in higher education: development, validation and testing of a specific tool. PloS One 15(7):e0235957. https://doi.org/10.1371/journal.pone.0235957

Article   CAS   PubMed   PubMed Central   Google Scholar  

Lee MC (2010) Explaining and predicting users’ continuance intention toward e-learning: an extension of the expectation–confirmation model. Comput Educ 54(2):506–516. https://doi.org/10.1016/j.compedu.2009.09.002

Li B, Jansen SJ, van der Heijden H, Jin C, Boelhouwer P (2022) Unraveling the determinants for private renting in metropolitan China: an application of the Theory of Planned Behavior. Habitat Int 127:102640. https://doi.org/10.1016/j.habitatint.2022.102640

Lin C-L, Jin YQ, Zhao Q, Yu S-W, Su Y-S (2021) Factors influence students’ switching behavior to online learning under COVID-19 pandemic: a push–pull–mooring model perspective. Asia-Pac. Educ Res 30(3):229–245. https://doi.org/10.1007/s40299-021-00570-0

Loehlin JC (2004) Latent variable models: an introduction to factor, path, and structural equation analysis. Psychology Press, East Sussex

MacFarlane K, Woolfson LM (2013) Teacher attitudes and behavior toward the inclusion of children with social, emotional and behavioral difficulties in mainstream schools: an application of the theory of planned behavior. Teach Teach Educ 29:46–52. https://doi.org/10.1016/j.tate.2012.08.006

Medina LC (2018) Blended learning: deficits and prospects in higher education. Australas J Educ Technol 34(1):42–56. https://doi.org/10.14742/ajet.3100

Mellikeche S, de Fatima Marin H, Benítez SE, de Lira ACO, de Quirós FGB, Degoulet P (2020) External validation of the unified model of information systems continuance (UMISC): an international comparison. Int J Med Inform 134:103927. https://doi.org/10.1016/j.ijmedinf.2019.07.006

Article   PubMed   Google Scholar  

Menant L, Gilibert D, Sauvezon C (2021) The application of acceptance models to human resource information systems: a literature review. Front Psychol 12:659421. https://doi.org/10.3389/fpsyg.2021.659421

Míguez-Álvarez C, Crespo B, Arce E, Cuevas M, Regueiro A (2020) Blending learning as an approach in teaching sustainability. Interact Learn Environ 28:1–16. https://doi.org/10.1080/10494820.2020.1734623

Mo C-Y, Wang C, Dai J, Jin P (2022) Video playback speed influence on learning effect from the perspective of personalized adaptive learning: a study based on cognitive load theory. Front Psychol 13:839982. https://doi.org/10.3389/fpsyg.2022.839982

Müller FA, Wulf T (2022) Blended learning environments and learning outcomes: the mediating role of flow experience. Int J Manag Educ 20(3):100694. https://doi.org/10.1016/j.ijme.2022.100694

Mustafa AS, Garcia MB (2021) Theories integrated with Technology Acceptance Model (TAM) in online learning acceptance and continuance intention: a systematic review. In: Proceeding of 2021 1st conference on online teaching for mobile education (OT4ME), Alcalá de Henares, 22–25 Nov 2021

Mustafa MH, Ahmad MB, Shaari ZH, Jannat T (2021) Integration of TAM, TPB, and TSR in understanding library user behavioral utilization intention of physical vs. E-book format. J Acad Librariansh 47(5):102399. https://doi.org/10.1016/j.acalib.2021.102399

Ocak MA (2011) Why are faculty members not teaching blended courses? Insights from faculty members. Comput Educ 56(3):689–699. https://doi.org/10.1016/j.compedu.2010.10.011

Oh JC, Yoon SJ (2014) Predicting the use of online information services based on a modified UTAUT model. Behav Inf Technol 33(7):716–729. https://doi.org/10.1080/0144929X.2013

Park C, Kim DG, Cho S, Han HJ (2019) Adoption of multimedia technology for learning and gender difference. Comput Hum Behav 92:288–296. https://doi.org/10.1016/j.chb.2018.11.029

Patel KJ, Patel HJ (2018) Adoption of internet banking services in Gujarat: an extension of TAM with perceived security and social influence. Int J Bank Mark 36:147–169. https://doi.org/10.1108/IJBM-08-2016-0104

Popa D, Repanovici A, Lupu D, Norel M, Coman C (2020) Using mixed methods to understand teaching and learning in Covid 19 times. Sustainability 12(20):8726. https://doi.org/10.3390/su12208726

Article   CAS   Google Scholar  

Porter WW, Graham CR, Spring KA, Welch KR (2014) Blended learning in higher education: institutional adoption and implementation. Comput Educ 75:185–195. https://doi.org/10.1016/j.compedu.2014.02.011

Prasad PWC, Maag A, Redestowicz M, Hoe LS (2018) Unfamiliar technology: reaction of international students to blended learning. Comput Educ 122:92–103. https://doi.org/10.1016/j.compedu.2018.03.016

Preacher KJ, Hayes AF (2008) Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav Res Methods 40(3):879–891. https://doi.org/10.3758/BRM.40.3.879

Pulham E, Graham CR (2018) Comparing K-12 online and blended teaching competencies: a literature review. Distance Educ 39(3):411–432. https://doi.org/10.1080/01587919.2018.1476840

Rasheed RA, Kamsin A, Abdullah NA (2020) Challenges in the online component of blended learning: a systematic review. Comput Educ 144:103701. https://doi.org/10.1016/j.compedu.2019.103701

Scherer R, Siddiq F, Tondeur J (2019) The technology acceptance model (TAM): a meta-analytic structural equation modeling approach to explaining teachers’ adoption of digital technology in education. Comput Educ 128:13–35. https://doi.org/10.1016/j.compedu.2018.09.009

Shah R, Goldstein SM (2006) Use of structural equation modeling in operations management research: looking back and forward. J Oper Manag 24(2):148–169. https://doi.org/10.1016/j.jom.2005.05.001

Shalender K, Sharma N (2021) Using extended theory of planned behaviour (TPB) to predict adoption intention of electric vehicles in India. Environ Dev Sustain 23(1):665–681. https://doi.org/10.1007/s10668-020-00602-7

Sharma SK (2019) Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: a SEM-neural network modeling. Inf Syst Front 21(4):815–827. https://doi.org/10.1007/s10796-017-9775-x

Sreen N, Purbey S, Sadarangani P (2018) Impact of culture, behavior and gender on green purchase intention. J Retail Consum Serv 41:177–189. https://doi.org/10.1016/j.jretconser.2017.12.002

St Quinton T (2022) The impact of past behaviour on social cognitive factors and sports participation in university students. Psychol Health Med 27(6):1193–1204. https://doi.org/10.1080/13548506.2020.1847304

Straub D, Limayem M, Karahanna-Evaristo E (1995) Measuring system usage: Implications for IS theory testing. Manage Sci 41(8):1328–1342. https://doi.org/10.1287/mnsc.41.8.1328

Šumak B, Heričko M, Pušnik M (2011) A meta-analysis of e-learning technology acceptance: the role of user types and e-learning technology types. Comput Hum Behav 27(6):2067–2077. https://doi.org/10.1016/j.chb.2011.08.005

Sun Y, Wang N, Guo X, Peng Z (2013) Understanding the acceptance of mobile health services: a comparison and integration of alternative models. J Electron Commer Res 14(2):183–200

Sungur-Gül K, Ateş H (2021) Understanding pre-service teachers’ mobile learning readiness using theory of planned behavior. Educ Technol Soc 24(2):44–57

Tang T, Abuhmaid AM, Olaimat M, Oudat DM, Aldhaeebi M, Bamanger E (2020) Efficiency of flipped classroom with online-based teaching under COVID-19. Interact Learn Environ 28:1–12. https://doi.org/10.1080/10494820.2020.1817761

Tao D, Fu P, Wang Y, Zhang T, Qu X (2022) Key characteristics in designing massive open online courses (MOOCs) for user acceptance: an application of the extended technology acceptance model. Interact Learn Environ 30(5):882–895. https://doi.org/10.1080/10494820.2019.1695214

Tao D, Yuan J, Shao F, Li D, Zhou Q, Qu X (2018) Factors affecting consumer acceptance of an online health information portal among young internet users. CIN-Comput Inform Nurs 36(11):530–539

Taylor AB, MacKinnon DP, Tein JY (2008) Tests of the three-path mediated effect. Organ Res Methods 11(2):241–269. https://doi.org/10.1177/1094428107300344

Teo T (2009) Evaluating the intention to use technology among student teachers: a structural equation modeling approach. Int J Technol Teach Learn 5(2):106–118

Teo T, Dai HM (2022) The role of time in the acceptance of MOOCs among Chinese university students. Interact Learn Environ 30(4):651–664. https://doi.org/10.1080/10494820.2019.1674889

Thompson RL, Higgins CA, Howell JM (1991) Personal computing: toward a conceptual model of utilization. MIS Quart 15(1):125–143. https://doi.org/10.2307/249443

Turvey K, Pachler N (2020) Design principles for fostering pedagogical provenance through research in technology supported learning. Comput Educ 146:103736. https://doi.org/10.1016/j.compedu.2019.103736

Tweneboah-Koduah EY, Adams M, Acheampong G (2019) The role of theories in social marketing in predicting physical activity behavior among the youth. J Soc Market 9(4):398–417. https://doi.org/10.1108/JSOCM-01-2018-0005

UNESCO. (2020). COVID-19 educational disruption and response. https://en.unesco.org/news/covid-19-educational-disruption-and-response Accessed 7 Jan 2022

Vafaei-Zadeh A, Wong TK, Hanifah H, Teoh AP, Nawaser K (2022) Modelling electric vehicle purchase intention among generation Y consumers in Malaysia. Res Transp Bus Manag 43:100784. https://doi.org/10.1016/j.rtbm.2022.100784

Venkatesh V, Bala H (2008) Technology acceptance model 3 and a research agenda on interventions. Decis Sci 39(2):273–315. https://doi.org/10.1111/J.1540-5915.2008.00192.X

Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manage Sci 46(2):186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Quart 27:425–478. https://doi.org/10.2307/30036540

Virani SR, Saini JR, Sharma S (2020) Adoption of massive open online courses (MOOCs) for blended learning: the Indian educators’ perspective. Interact Learn Environ 28:1–17. https://doi.org/10.1080/10494820.2020.1817760

Wang Y, Dong C, Zhang X (2020) Improving MOOC learning performance in China: an analysis of factors from the TAM and TPB. Comput Appl Eng Educ 28(6):1421–1433. https://doi.org/10.1002/cae.22310

Wu B, Chen X (2017) Continuance intention to use MOOCs: integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Comput Hum Behav 67:221–232. https://doi.org/10.1016/j.chb.2016.10.028

Wu H, Luo S (2022) Integrating MOOCs in an undergraduate English course: students’ and teachers’ perceptions of blended learning. SAGE Open 12(2):21582440221093035. https://doi.org/10.1177/21582440221093035

Wu J, Liu W (2013) An empirical investigation of the critical factors affecting students’ satisfaction in EFL blended learning. J Lang Teach Res 4(1):176–181. https://doi.org/10.4304/JLTR.4.1.176-185

Xu XL, Shen WQ, Islam AA, Shen JY, Gu XQ (2021) Modeling Chinese teachers’ behavioral intention to use recording studios in primary schools. Interact Learn Environ 29:1–18. https://doi.org/10.1080/10494820.2021.1955713

Yang CC, Ogata H (2022) Personalized learning analytics intervention approach for enhancing student learning achievement and behavioral engagement in blended learning. Educ Infion Technol 27:1–20. https://doi.org/10.1007/s10639-022-11291-2

Yang Y, Zhang H, Chai H, Xu W (2022) Design and application of intelligent teaching space for blended teaching. Interact Learn Environ 30:1–18. https://doi.org/10.1080/10494820.2022.2028857

Yin B, Yuan CH (2021) Precision teaching and learning performance in a blended learning environment. Front Psychol 12:631125. https://doi.org/10.3389/fpsyg.2021.631125

Yoon HY (2016) User acceptance of mobile library applications in academic libraries: an application of the technology acceptance model. J Acad Librariansh 42(6):687–693. https://doi.org/10.1016/j.acalib.2016.08.003

Yousafzai SY, Foxall G, Pallister JG (2007) Technology acceptance: a meta‐analysis of the TAM: Part 1. J Model Manag 2:251–280. https://doi.org/10.1108/17465660710834453

Yu Q, Liu L, Tang Q, Wu W (2021) Online teaching-present situation and its future: a survey of online study for medical students during the COVID-19 epidemic. Ir Educ Stud 40(2):207–215. https://doi.org/10.1080/03323315.2021.1916557

Yuzhanin S, Fisher D (2016) The efficacy of the theory of planned behavior for predicting intentions to choose a travel destination: a review. Tour Rev 71:135–147. https://doi.org/10.1108/TR-11-2015-0055

Zaremohzzabieh Z, Ahrari S, Krauss SE, Samah AA, Meng LK, Ariffin Z (2019) Predicting social entrepreneurial intention: a meta-analytic path analysis based on the theory of planned behavior. J Bus Res 96:264–276. https://doi.org/10.1016/j.jbusres.2018.11.030

Zhang T, Tao D, Qu X, Zhang X, Lin R, Zhang W (2019) The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transp Res Pt C-Emerg Technol 98:207–220. https://doi.org/10.1016/j.trc.2018.11.018

Zhang W, Wang Y, Yang L, Wang C (2020) Suspending classes without stopping learning: China’s education emergency management policy in the COVID-19 outbreak. J Risk Financ Manag 13(3):55. https://doi.org/10.3390/jrfm13030055

Zhou S, Zhou Y, Zhu H (2021) Predicting Chinese university students’ e-learning acceptance and self-regulation in online English courses: evidence from Emergency Remote Teaching (ERT) during COVID-19. SAGE Open 11(4):21582440211061379. https://doi.org/10.1177/21582440211061379

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This research was supported by the National College Student Innovation and Entrepreneurship Training Program of China (Grant No. 202210337027, No. 202210337003), the College Student Innovation and Entrepreneurship Training Program of Zhejiang University of Technology (Grant No. 2020011) and Higher Education Research Project of the “14th Five-Year Plan” of Guangdong Association of Higher Education in 2022 (Grant No. 22GYB158).

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Yu, T., Dai, J. & Wang, C. Adoption of blended learning: Chinese university students’ perspectives. Humanit Soc Sci Commun 10 , 390 (2023). https://doi.org/10.1057/s41599-023-01904-7

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CHAPTER 2: Theories Supporting Blended Learning

Grounding our practice in theory will help us make better decisions when implementing blended learning and support our learners more effectively to achieve deep and meaningful learning. In this chapter, we review two main theoretical frameworks that can be applied to blended learning, then consider several models of blended learning and technology integration.

Introduction

As most of us around the world have done the majority of our learning in person and in classrooms, we usually refer to the combination of in-person and online teaching as a special form of learning called “blended.” Someday, however, we expect this form will become the standard, and we will drop the term “blended learning” altogether.

Blended learning “is part of the ongoing convergence of two archetypal learning environments” (Bonk & Graham, 2006, p. 2). However, the influences of the two types of delivery are not equal, and how to blend looks different if you are starting from an in-person school to how it looks if you are coming from a distance education background.

Traditional face-to-face, in-person, classroom-based teaching and learning has been used for centuries as the ubiquitous delivery method. Distance and distributed teaching and learning opportunities are much newer, particularly in reference to technology-enabled learning. When online education became available, it was used first in distance education, with students studying fully online. Notions of blending classroom-based learning and online or distance education came later.

Only over the last few decades has technology for learning been readily available. It emerged so quickly that use of these technologies was implemented well before we had substantial knowledge of its impact and the differences it made for teachers and students. Now, with more evidence, improved theories and models, and more clarity about how to use both in-person and online teaching and learning, we can blend the two delivery modes with careful attention to each.

Using Theory to Support Blended Learning Practice

Why is theory important?Effective blended learning is more than just tips and techniques;understanding the key concepts in blended learning and what makes it successful are important.First,we will talk about theory and conceptual frameworks for blended learning;the tips will come later!

It is not possible to review all models of blended learning here. Wang, Han and Yang (2015) provide an important overview of all major blended learning theoretical frameworks available. Our focus in this chapter will be on two frameworks: the Complex Adaptive Blended Learning System and the Community of  Inquiry .

These two models take a comprehensive view of the design and implementation of blended learning. They are applicable to blended learning in any segment of education, with appropriate adjustments as necessary based on learners’ needs and characteristics, whether you are a teacher or instructor in K–12 schools, colleges and universities, the military, the industrial workplace or the corporate world.

The Complex Adaptive Blended Learning System

Figure 2.1 presents a diagram that outlines all the components of the Complex Adaptive Blended Learning System, or CABLES framework. The learner sits at the center of the model, but all components impact each other. There are six elements in the system, all with their own sub-systems. These six elements are:

• the learner
• the teacher
• the technology
• the content
• the learning support
• the institution

Not only does each element have its own character and subsystem, but each acts in relationship to all the others. As in any complex system, the relationships are dynamic and integrative. This adaptive system of blended learning emerges from the relationships and the effects of each element acting with and on the other elements.

Table 2.1. The six elements of the CABLS framework.

The CABLS framework is designed to “facilitate a deeper, more accurate understanding of the dynamic and adaptive nature of blended learning” (Wang et al., 2015, p. 390). This systems approach allows someone new to blended learning to consider key interacting components at work as they create and offer a blended learning course or programme. Teachers will be most interested in the relationship between content, learners and technology. For more on designing with interacting components, see Richardson et al. (2012).

The Community of Inquiry Theoretical Framework in Blended Learning

In 2000, Garrison, Anderson and Archer published a theoretical framework developed to structure the process of learning in an online or blended environment. The Community of Inquiry (CoI), a model of inquiry-based teaching and learning, is based on the work of John Dewey and constructive views of experiential learning.

The CoI framework describes the necessary elements to create deep and meaningful learning. The original framework identifies the education experience as occurring at the convergence of three presences: cognitive , teaching and social . In our application of this model, presence is defined as a state of alert awareness, receptivity and contentedness to the social, cognitive, emotional and physical workings of both the individual and the group in the context of their learning environments (adapted from a definition by Rodgers and Raider-Roth, 2006, p. 1).

Inquiry-based teaching and learning is more important now than ever before, as both a process for learning and a subject for learning to learn. Inquiry-based teaching and learning has its roots in the new learning movement of the 1960s, the time of the so-called “me generation.” This call for more active learning drew insight from foundation thinkers in education like Dewey (1938) and Vygotsky (1997), who saw the use of individual experience and the construction of one’s own knowledge structures as key to engagement and learning outcomes. Now called inquiry-based learning by way .of contrast to content-based learning, learning through cognitive engagement allows students more control over the way they develop a knowledge base. Beyond content acquisition, inquiry-based learning is seen as a key opportunity for developing competence in higher-order thinking skills (Garrison, 2016). Passive, amateur learners are not part of inquiry-based learning. Inquiry-based teaching, then, requires a focus on providing meaningful engagement opportunities rather than direct instruction about content; the latter supports and fosters passive learning.

Inquiry-based teaching also requires making the learning process explicit. Building on the early work of Schwab (1966), this teaching practice offers structure to move learners through active inquiry processes. For Schwab, the active inquiry process starts by using questions, problems and material to invite learners to identify relationships between concepts or variables. As learners advance, questions or problems are presented and the learners discover the path to answers themselves. As a third and final stage, a topic is presented, and learners themselves identify questions, problems, methods and answers while the teacher provides guidance and facilitates learning.

Creating a Community of Inquiry: What the Research Tells Us

The CoI framework supports guided inquiry by identifying teaching activity and provides guidance, based on theory and practice, on content and processes for blended learning.

In keeping with the original three presences of the CoI framework (social presence, cognitive presence and teaching presence), blended learning using the CoI framework creates opportunities for self-reflection, active cognitive processing, interaction and peer-teaching. In addition, expert guidance from teachers at the right time encourages engagement and shared application activities, highlighting the importance of creating communities of inquiry in the classroom — whether face-to-face, online or blended.

Creating communities of inquiry in blended learning is one of the most researched pedagogical approaches in universities and colleges. The original Garrison, Anderson and Archer (2000) article explaining this framework has been cited in the scholarly literature over 4,000 times. Much of the early research focused on understanding social presence (Richardson & Swan, 2003) as a new way to approach teaching beyond strict transmission models of delivery. A significant amount of research has also been done to measure the components of this framework and how they operate in reference to one another (Arbaugh et al., 2008; Garrison, Cleveland-Innes & Fung, 2010). A recent analysis of the literature identified that in measuring and applying the CoI, “the most frequently used and the one adopted the most commonly in the literature is the CoI survey instrument developed by Arbaugh et al. (2008)” (Olpak, Yagci & Basarmak, 2016, p.  1090).

Accurate measurement of the framework allows for a more detailed examination of cognitive presence. This is important, as none of the presences stand alone. Cognitive presence emerges out of four distinct but overlapping components of practical inquiry: triggering events, exploration, integration and resolution. Establishing deep and meaningful learning requires activity in all four components. However, Akyol and Garrison (2011) report evidence that cognitive presence requires a balance among cognitive, social and teaching presence. Direct instruction and facilitation of cognitive activity, beyond just explaining content, is a key role for teachers using this framework. This corroborates Archibald’s (2010) evidence that teaching presence and social presence explain 69% of the variance in cognitive presence.

Teaching presence, rather than “teacher presence,” is so named to allow for a teaching function for both teachers and students in a CoI. While the teacher, or instructor of record, plays a leadership role, teaching presence allows for and fosters peer-teaching among students. Recent studies clarify the importance of teaching presence in the generation of satisfying learning experiences among students (Chakraborty & Nafukho, 2015; Morgan, 2011; Shea, Hayes & Vickers, 2010). It is, however,  linked to other presences in a significant way. For example, Shea and Bidjerano (2009) report evidence that the student experience of teaching presence affects the emergence of social presence.

In addition to these three presences, emotional presence has been suggested (Cleveland-Innes & Campbell, 2012; Stenbom, Cleveland-Innes & Hrastinski, 2016). Emotional presence is defined as the outward expression of emotion, affect and feeling, by individuals and among individuals in a community of inquiry, as they relate to and interact with the learning technology, course content, students and instructor. Item indicators for emotional presence have been analysed with the instrument measuring the original three presences (Arbaugh et. al, 2008). Exploratory factor analysis suggests emotional presence may stand alone as a separate element in this framework (Cleveland-Innes, Ally, Wark & Fung, 2013). Further research is required to evaluate the relationship between emotional presence and other elements in the framework.

Seven Blended Learning Structures in Education

Now that you have a view on the theory underlying blended learning, we can discuss more concrete applications of types of instruction.

Many factors must be considered when choosing how to blend in-person and online teaching and learning activities. In some cases, most interactions between students and the teacher, as well as the direct delivery of instruction, take place in person in the classroom, while materials and possibly some additional activities are delivered online. In other cases, most of the class activities occur online, with infrequent meetings in person to solve problems and support community building. In some blended arrangements, students may choose which activities to complete online and which to complete in a classroom.

Ideally, blends are personalized so individual students have the blend that best fits their age, life circumstances and learning needs. These are called à la carte models. Students choose what to take fully online, what to take fully in person and, when the design is available, blended courses where they choose when to go to in-person classes and when to watch videos, download readings and complete assignments online.

This kind of personalization is not always available. Most important is ensuring that students are able to function well as learners with any delivery method, single-mode or blended, even if it is not their preference or the best situation for them. Teachers are valuable coaches for helping students manage  in any learning situation; it is up to teachers and learning designers to offer blended activities that best suit the subject, the learners’ needs and the curriculum requirements. Not all unique and interesting blended learning designs are one-size-fits-all model.

Below are seven sample configurations of blended learning activities, offered by O’Connell (2016) for you to consider for your teaching situation. These examples of blended learning are drawn from higher education but can be shaped to fit any teaching and learning situation. Chapter 3 will provide further information about creating your own unique design of blended learning.

• Blended face-to-face class: Also sometimes called the “face-to-face driver model,” the blended face-to-face class model is based in the classroom, although a significant amount of classroom time has been replaced by online activities. Seat time is required for this model, while online activities are used to supplement the in-person classes; readings, quizzes or other assessments are done online at home. This model allows students and faculty to share more high-value instructional time because class time is used for higher-order learning activities such as discussions and group projects.
• Blended online class: Sometimes referred to as the “online driver model,” this class is the inverse of the blended face-to-face class. The class is mostly conducted online, but there are some required in-person activities such as lectures or labs.
• The flipped classroom: The flipped classroom reverses the traditional class structure of listening to a lecture in class and completing homework activities at home. Students in flipped classes watch a short lecture video online and come into the classroom to complete activities such as group work, projects or other exercises. The flipped classroom model can be seen as a sub-model of the blended face-to-face or blended online class.
• The rotation model: In this model, students in a course rotate between various modalities, one of which is online learning. There are various sub-models: station rotation , lab rotation and individual rotation . Some of these sub-models are better suited to K–12 education; station rotation, for example, requires students to rotate between stations in the classroom at an instructor’s discretion. Others work well on a college campus; the lab rotation model, for example, requires students in a course to rotate among locations on campus (at least one of which is an online learning lab). In the individual rotation model, a student rotates through learning modalities on a customized schedule.
• The self-blend model: While many of the blended learning models on this list are at the course level, self-blending is a programmer-level model and is familiar to many college students. Learners using this model are enrolled in a school but take online courses in addition to their traditional face-to-face courses. They are not directed by a faculty member and choose which courses they will take online and which they will take in person.
• The blended MOOC: The blended MOOC is a form of flipped classroom using in-person class meetings to supplement a massive open online course. Students access MOOC materials— Perhaps from another institution or instructor if the course is openly accessible — outside o f  class and then come to a class meeting for discussions or in-class activities. In 2012, according to Campus Technology, San Jose State University piloted a blended MOOC using MITs Circuits and Electronics course, with students taking the MOOC out of class while face-to-face time was used for additional problem solving (LA Martina, 2012).
• Flexible-mode courses: Flexible-mode courses offer all instruction in multiple modes — in person and online — and students choose how to take their course. An example of this is San Francisco State University’s hybrid flexible (HyFlex) model, which offers classroom-based and online options for all or most learning activities, allowing students the ability to choose how they will attend classes: online or in person (Beastly, 2016).

Evaluations are sparse but are now under way , testing different types of blended learning models for results — see, for example, Stockwell, Stockwell, Cennamo and Jiang (2015).

Blended Learning as Technology-Enabled Learning in the Classroom

Another type of blend adds technology in the classroom. Often called technology-enabled learning, adding technology to in-person teaching and learning may foster engagement and improve learning outcomes. The SAMR model, well-suited for K–12, is an approach for the progressive implementation of new technology.

Table 2.2. SAMR descriptors

The following graphic illustrates these comments,using types of coffee as examples.

In this chapter, we have laid the theoretical foundations for the successful implementation of blended learning, with a special focus on two frameworks: the Complex Adaptive Blended Learning System and the Community of Inquiry.

The CABLS framework analyses learning into a complex and dynamic system of six interacting elements: learner, teacher, technology, content, learner support and institution. The CoI emphasizes inquiry-based  teaching, describing meaningful learning as the convergence of cognitive, teaching and social presences, with emotional presence as a potential fourth component. Both frameworks can provide guidance in developing blended learning content and processes to support active, lifelong learners.

We have also looked at seven sample configurations of blended learning design: blended face-to-face class, blended online class, flipped classroom, rotation model, self-blend model, blended MOOC and flexible-mode courses. When it comes to blended learning models, one size does not fit all; teachers and learning designers should offer blended learning activities to best suit the content, the learners’ needs and the curriculum requirements.

Finally, we considered the relationship between blended learning and technology-enabled learning, using the SAMR model — substitution, augmentation, modification and redefinition — to describe how technology can be progressively integrated into the classroom.

Guided by these theoretical frameworks and models, we turn in Chapter Three to the development of purposeful and successful blended learning, from initial instructional design decisions to evaluation.

REFLECTION  QUESTIONS

• Identify the six components of the CABLES framework as applied to your own teaching setting. How might this framework support improvements in your teaching setting?
• Consider your own course as you review the questions in the CoI Survey found in Appendix 1. Are all three presences — cognitive, teaching and social — represented?
• Consider the seven blended learning structures as they might be used in your own teaching setting. Which do you think would be most successful? Which do you find most appealing in your context? Design your own model of blended learning.
• Consider the SAMR model as it might apply to your teaching setting. At which stage of technology integration are you currently? What might it take to move to the next stage?

Resources for Further Reading

The  Community of Inquiry resource site, including an overview of the CoI framework, survey and key publications. Retrieved from http://coi.athabascau.ca/

Common Sense Education. (2016). Introduction to the SAMR model. Common Sense Education .  Retrieved  from https://www.youtube.com/watch?v=9b5yvgKQdqE

O’Connell, A. (2016). Seven blended learning models used today in higher ed. Retrieved from http://acrobatiq.com/seven-blended-learning-models-used-today-in-higher-ed/

Wang, Y., Han, X., & Yang, J. (2015). Revisiting the blended learning literature: Using a complex adaptive systems framework. Journal of Educational Technology & Society, 18 (2), 380–393. Retrieved from https://www.j-ets.net/ETS/journals/18_2/28.pdf

Guide to Blended Learning by Commonwealth of Learning is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License , except where otherwise noted.

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Effectiveness of Blended Learning in Nursing Education

María consuelo sáiz-manzanares.

1 Departamento de Ciencias de la Salud, Facultad de Ciencias de la Salud, Universidad de Burgos, C/ Comendadores s/n, 09001 Burgos, Spain; se.ubu@ralocsec

María-Camino Escolar-Llamazares

Álvar arnaiz gonzález.

2 Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Avda. Cantabria s/n, 09006 Burgos, Spain; se.ubu@garavla

Associated Data

Currently, teaching in higher education is being heavily developed by learning management systems that record the learning behaviour of both students and teachers. The use of learning management systems that include project-based learning and hypermedia resources increases safer learning, and it is proven to be effective in degrees such as nursing. In this study, we worked with 120 students in the third year of nursing degree. Two types of blended learning were applied (more interaction in learning management systems with hypermedia resources vs. none). Supervised learning techniques were applied: linear regression and k-means clustering. The results indicated that the type of blended learning in use predicted 40.4% of student learning outcomes. It also predicted 71.9% of the effective learning behaviors of students in learning management systems. It therefore appears that blended learning applied in Learning Management System (LMS) with hypermedia resources favors greater achievement of effective learning. Likewise, with this type of Blended Learning (BL) a larger number of students were found to belong to the intermediate cluster, suggesting that this environment strengthens better results in a larger number of students. BL with hypermedia resources and project-based learning increase students´ learning outcomes and interaction in learning management systems. Future research will be aimed at verifying these results in other nursing degree courses.

1. Introduction

In approximately the last decade there has been a marked interest in investigating ways of teaching other than traditional face-to-face. The incorporation of technological resources such as virtual platforms and hypermedia resources, combined with other innovative, methodological techniques such as project-based or problem-based learning, have revolutionized the teaching–learning process. The aim is to teach in the most efficient way possible and to make the most of resources while ensuring sustainability. These technological and methodological resources have been applied to different disciplines, especially in the field of health sciences (medicine, pharmacy, psychology, veterinary, etc.). However, in recent years these resources have been incorporated into nursing studies. Next, an approach will be made for the most relevant concepts of teaching in a virtual platform, which has been called blended teaching, as well as the implementation of methodological resources for project-based learning. Likewise, special importance will be given to its application for the formation of future nursing programs by analyzing the pros and cons of this form of teaching and learning in the society of the 21st century. For this reason, the most relevant concepts of these new forms of teaching and their specific application to the nursing degree will be dealt with below. The final objective of this work is to study the effectiveness of different blended learning environments in the teaching of future nurses.

Twenty-first century society requires students and graduates to develop a series of skills related to two important leitmotifs: collaborative work and operation of information and communications technology (ICT). It is increasingly necessary to possess effective and rapid problem-solving skills and to develop digital competences [ 1 ]. The use of learning management systems (LMS) is, therefore, a reference in instructional practice, especially in higher education, as is the implementation of collaborative work in these methodological settings for the resolution of tasks and problems. A good example might be the use of project-based learning (PBL) methodology [ 2 ]. Recent investigations have confirmed that if such a methodology is accompanied by the use of hypermedia resources (e.g., flipped learning experiences, quizzes, use of wikis, on-line glossaries, etc.), then acquisition of deep learning is strengthened in students [ 3 ]. Deep learning is a concept developed in the framework of the taxonomy of Bloom [ 4 ]. It corresponds to the highest level of learning competences (comprehending, analyzing, summarizing, and evaluating their own learning). One of the currents of thought in LMS learning environments suggests that learning in these environments implies deeper learning from the point of view of cognitive and metacognitive complexity, as these facilitate self-regulated learning (SRL) and meaningful learning [ 5 ].

Likewise, LMS permit a more precise analysis of interactions which are logged in records (or logs). The logs represent units of information and registration that stores precise data on the frequency of user interactions and their duration [ 6 ]. LMS also facilitates the inclusion of hypermedia resources [ 7 ]. The use of these resources is especially relevant in health science degrees (nursing, medicine, pharmacy, etc.) since it implements practical assumptions in the work, which has been proven to reduce errors in the workplace [ 8 ].

There are various stages in this instruction process that will facilitate or inhibit the efficiency and depth of the learning process. One of them is the design of learning tasks in LMS [ 9 , 10 ]. Another essential element is that the teacher plans for process-oriented feedback [ 11 ].

1.1. Teaching through Learning Management Systems

The teacher has to reflect, among other points, on the following points: (1) the aims of the subject module, (2) to whom it is addressed, (3) what previous knowledge is required for a successful approach to the subject matter, (4) the type of learning tasks that facilitate content acquisition, (5) the metacognitive skills of the students prior to the instruction, (6) the cognitive and the metacognitive skills in each task needed for its effective solution, and (7) when and where the teaching–learning process will be developed. Likewise, the teacher has to plan follow-up with both the individual student and the group behavior on the platform. As has been argued, solving problems in a collaborative way is one of the most demanding skills in 21st century society. These types of competences are key references in educational and technological areas and for entry into employment. Collaborative work facilitates the construction of deep and effective learning in the students [ 10 ]. A scheme for the preparation of pedagogic design in the LMS may be seen in Table 1 .

Preliminary elements to take into account for the design of learning activities.

Nevertheless, computational techniques are required to conduct a conclusive analysis of student behavior in LMS. As previously mentioned, at present a broad percentage of learning is done in virtual environments, in what is called blended learning teaching. A lot of data can be recorded by LMS and accessed through logs. However, educational data mining (EDM) [ 12 , 13 ] is needed to study them precisely. Machine learning techniques can be applied to EDM. Subsequently, possible applications of those techniques will be presented in the analysis of learning data in LMS environments.

1.2. Application of Artificial Intelligence Techniques to Analyze the Teaching and Learning Process

Development of the internet and information and communications technology (ICT) has expanded learner access to information, and they have changed the way that information is taught and the way it is learned [ 14 ]. A learning management system (LMS) is an interactive learning environment that facilitates both teaching and learning. In addition, these software environments record all the actions performed by the teacher and by the students, under individual and group headings. However, those logs store a lot of data and learning analytics have to be used in order to study them in a flexible and accurate manner. These techniques can be simple, such as the ones usually found in LMS (descriptive statistics). However, more complex analytical techniques can be used, such as machine learning techniques (a subset of artificial intelligence). The latter are analogous to the computational thought of the human brain and operate with what is known as artificial intelligence. Machine learning techniques of classification and clustering [ 15 ] are among the most widely applied techniques for data analysis in educational environments. The use of these techniques for the analysis of both student and teacher behaviors will provide the teacher and those responsible for educational institutions with ideas to introduce improvements into the learning environment [ 11 ].

In brief, machine learning techniques are used, as these techniques are currently considered to provide the researcher with more data in the field of cognitive psychology and learning than traditional statistical techniques [ 16 , 17 ]. In particular, machine learning techniques permit personalized learning and provide individualized information on the development of student learning. Prediction techniques facilitate early detection of at-risk students and, therefore, personalized [ 18 ] help from the teacher [ 19 , 20 ]. Machine learning techniques also provide information on the effects of predicting the independent variable over each of the dependent variables in percent effects [ 21 ].

1.3. Design of the Blended-Learning Space in Nursing Instruction

Blended teaching, increasingly present in educational scenarios, is done through a blend of face-to-face (F2F) and virtual learning on LMS, known as blended learning. However, there is no generalized agreement on the taxonomy of blended learning [ 22 ]. Nevertheless, its differences with blended learning are accepted; in the blended learning environment, the student completes 80% with LMS and 20% is F2F, hereafter referred to as Blended Learning type 1. In contrast, blended learning (80% interaction in the LMS) is a space where feedback is done 80% of the time through F2F and 20% through LMS [ 18 , 23 ], hereafter referred to as Blended Learning type 2. Recent investigations have found that the replacement blended environment accompanied by the use of active methodologies (e.g., PBL, use of hypermedia resources, flipped learning experiences, and quizzes, or all at once) improved the learning results of students [ 3 , 24 ]. These achievements are especially significant in university environments [ 25 , 26 ] because future graduates will have to develop collaborative work skills, problem-solving independence, and the use of new technologies. These skills are essential for good development of entrepreneurship.

Along these lines, recent studies have shown that [ 27 ] an educational intervention that applies blended learning methodology can easily be added into nursing curricula. This type of learning enhances learning in this field. Recent systematic research indicated that blended learning together with PBL is a methodology that ensures effective learning among nursing students [ 28 ]. This type of paradigm is more effective than traditional teaching such as face to face. The reasons are that students need to develop the knowledge and skills necessary in clinical practice. Several studies recommend nursing teachers to use multifaceted techniques (blended learning, learning based in projects, etc.) to promote effective learning beyond face-to-face teaching [ 29 ]. While these studies highlight the need to train teachers in these techniques [ 28 ], the main reason is that, traditionally, teaching has been done face to face, and an organized transfer towards the use of these methodological resources is needed. A recent systematic review showed that, since 2018, there has been a growing interest in the implementation of these experiences in nursing studies. However, an increase in these experiences and more research in this discipline of knowledge are needed [ 30 ].

Moreover, blended learning environments permit an evaluation of the whole teaching–learning process in a systematic and simple way. Thus, the suggestion is that there are different variables that influence successful learning in this line of investigation into e-evaluation models, especially the learning strategies employed by the students themselves [ 31 , 32 ], the environment in which the learning takes place [ 33 ], the teaching design that the teacher brings to the class [ 30 ], and the behavioral learning of the students in the LMS [ 23 ]. The prediction interval of these variables is situated around 56%-61% [ 34 ].

1.4. Extraction and Analysis of Information on the Teaching–Learning Process Recorded in LMS

As we have mentioned earlier, development of the teaching through LMS will facilitate the student in learning recording and follow-up behaviours [ 35 ]. Many of these learning managers use supervised machine learning techniques, techniques such as multiple regression analysis (MRA), neural network, and SVM. Those techniques help with the detection and subsequent prediction of successful and risk behaviours. Behaviours of the students in LMS that have been related to successful learning are, among others [ 23 ]:

• the time that is used in carrying out the tasks;
• student time expended on studying theoretical content;
• the results in the self-evaluation test (quiz efforts);
• the quality of forum discussions (type and length of message);
• time employed in analysing the feedback given by the teacher;
• the number and type of messages sent;
• the frequency of access to LMS;
• contribution to content creation;
• files opened; and
• delivery time of the activities.

Therefore, the frequency and systematicness of student interactions and their interactions with LMS are directly related to effective learning [ 36 ]. Along these lines, recent investigations [ 23 ] have revealed differences in predicting learning results in relation to the variable “teaching methodology” (understood in terms of the pedagogic structure of the teaching, the evaluation procedures, and feedback). The type of activities and the evaluation tests (quizzes, tests, projects, presentations…) are understood to determine the effectiveness of behavioural learning logged on the LMS.

As previously mentioned, application of machine learning techniques to study the logs will allow the teachers to analyze the behavioural learning of their students and to detect at-risk students. In these cases, early intervention will presumably improve student learning responses. Recent studies have confirmed [ 17 , 35 ] that following up with student behavioural learning in the LMS facilitates the identification of at-risk students with an explained variance of 67.2%.

In summary, the use of machine learning techniques will permit the study of behavioral learning of both students and teachers on the platform, which will facilitate the application of prediction techniques to the learning results [ 37 ]. Reviewing the investigations presented earlier, we consider it important to study the behavior of PBL in the LMS. As has been indicated, there are few studies in that field, and more information is needed that will help to improve teaching practices in these environments [ 38 ]. Project work and personalization of learning in LMS have been proven to have significant effects on the quality of learning. Particular relevance has been in Health Science degrees, such as nursing or medicine, etc., since it facilitates work on clinical cases in a collaborative way and optimizes the results applied to real learning contexts [ 39 ].

This research study was performed to analyze data of students’ online and face-to-face (F2F) activity in a blended nursing learning course. We applied two types of blended learning: Blended Learning type 1 [in which the interaction between the teacher and students is 80% in the LMS and 20% Face to Face (F2F)] and Blended Learning type 2 [in which the interaction between the teacher and students is 20% in the LMS and 80% Face to Face (F2F)].

In light of the above, the hypotheses in this study were the following:

H 1: The types of blended learning (Blended Learning type 1 vs. Blended Learning type 2) used will predict student learning outcomes;

H 2: The types of blended learning (Blended Learning type 1 vs. Blended Learning type 2) used will predict the learning behaviours logged on the LMS; and

H 3: The type of clusters will be different for each type of blended learning used (Blended Learning type 1 vs. Blended Learning type 2).

2. Materials and Methods

2.1. design.

A quasi-experimental post-treatment design with an equal control group (in terms of metacognitive skill) was used. Likewise, learning outcomes (learning outcomes in the development of project-based learning; learning outcomes in exhibition of project-based learning; learning outcomes in the test; and learning outcomes total) and behavioral learning in the LMS were the dependent variables (access to complementary information; Access to guidance to prepare PBL; Access to theoretical information; Access to teacher feedback; and mean visits per day).

2.2. Participants

A sample of 120 university students was assembled following the third year of their nursery degree in Spain (the degree has four years) during one semester (9 weeks): 63 followed the Blended Learning type 1 methodology and 57 followed the Blended Learning type 2 methodology (see Table 2 ). The students were assigned to each blended learning group (Blended Learning type 1 vs. Blended Learning type 2) by means of convenience sampling. The work was developed in the subject of “Quality management methodology of nursing services.”

Group assignment and descriptive statistics for age, a n = 60. b n = 62.

Note. M age = Mean Age; SD age = Standard Deviation.

2.3. Instruments

a. LMS UBUVirtual version 3.1 . A Moodle-based learning management system (LMS) was used that began with a constructivist approach and was developed through a modular system. It is a personalized Moodle-based LMS. An LMS is a modular learning environment that permits interaction and feedback between teacher and students, in many cases in real time, and in addition it facilitates the process of automated feedback.

b. The (ACRAr) Scales of Learning Strategies by Román & Poggioli [ 40 ]. This widely tested instrument identifies 32 strategies at different points in the information processing cycle. The reliability indicators on the scale were between α = 0.75 to α = 0.90 and the indicators of content validity were between r = 0.85 and r = 0.88. The subscale of metacognitive skills was applied in this study; this scale incorporated 17 strategies about the use of metacognitive skills into the problem solving tasks. A reliability index of α = 0.80 was obtained in this study; the reliability indicator on this subscale was α = 0.90 and the indicator of validity was r = 0.88.

c. Student learning results: the results were recorded in the different evaluation procedures . (1) Multiple-choice tests on the theoretical contents of the subject (test) were assigned a weight of 30% of the final grade. The test had 10 multiple-choice questions (four possible answers) with only one correct response. As well, five questionnaire-type quizzes were administered, one for each thematic unit. Cronbach’s Alpha reliability of the test was α = 0.81. (2) Development of PBL, with a weight of 25%, was measured with a rubric, which can be seen in Supplemental Material Table S1 . (3) Likewise, the exhibition of the PBL, with a weight of 20%, was also measured with a rubric and can be seen in Supplemental Material Table S2 . In the final mark, Cronbach’s Alpha reliability of PBL was α = 0.62. This result is lower because there was less dispersion among the scores in this type of evaluation test. Since the performance of the groups was quite uniform, this aspect can be checked in the results section and it is in accordance with the philosophy of PBL. Finally, the learning outcomes total covered the weighted scores of all the results (over 10 points). 4) The students solved five practices, and this part was 25% of the final grade. However, in this part all students had the highest qualification since the teacher reviewed the practices continuously, and if they were not correct the teacher ordered them to be repeated. Therefore, because it is not discriminate it has not been included in the analysis. Examples of the PBLs developed can be found at this link https://riubu.ubu.es/handle/10259/3753/discover .

2.4. Procedure

Convenience sampling was followed for the choice of the sample. This was due to the possibility of working with this methodology by a specialist teacher who attended to both groups, and in this way the "type of teacher" effect was avoided. Before the instructional intervention, the two groups (Blended Learning type 1 vs. Blended Learning type 2) were scored on the metacognitive skills Scale of ACRAr [ 40 ], with the aim of establishing the similarities between both groups in terms of metacognitive skills.

As stated in the introduction, Blended Learning type 1 was applied to the experimental group, a learning environment in which the interactions between teacher and student were 20% F2F and 80% LMS. Likewise, Blended Learning type 2 was applied to the control group, a learning environment in which the interactions between teacher and students were 20% LMS and 80% F2F. In the experimental Group, hypermedia resources were used such as videos, and feedback was through the LMS. In contrast, classroom interactions between teacher and students and feedback in the control group were all F2F. In both groups, PBL methodology was followed. The difference, as has been pointed out, consisted of the type of blended learning in use (Blended Learning type 1 vs. Blended Learning type 2). Project development was done in both (the control and the experimental) groups in a collaborative way. The project work was completed in small groups of students of between 2 and 5 members.

2.5. Data Analysis

The following statistical analyses were applied: (1) Analysis of asymmetry and kurtosis; (2) analysis of the variance of a fixed-effect factor (ANOVA); (3) multiple regression analysis (MRA) [appropriate Tolerance (T) values were considered close to one and, with respect to the variance inflation factor, the values were between 1–10]; (4) cluster analysis. Package for the Social Sciences (SPSS) v.24 was used to perform the different analyses [ 41 ]. Likewise, the Goodness-of-fit indices were measured by structural equation modeling (SEM) and was used to study the settings of the machine learning technique to predict the learning results. The calculations were performed with the Statistical Package for the Social Sciences (SPSS) AMOS v.24 [ 42 ]. (5) Finally, to visualize the results in a cluster analysis, RapidMiner Studio software [ 43 ] was used.

2.6. Ethical Considerations

The research project was approved by the Ethics Committee of the University of Burgos. Previously, at the start of the project, the students were informed of the objectives, and their participation was at all times on a voluntary basis. Likewise, informed consent of each participant was recorded in writing.

3.1. Previous Statistical Normalcy Analysis in the Sample

Before starting the research, the indicators of normality were studied. The results obtained from earlier statistical analyses with regard to the normality of the sample are presented below (values higher than |2.00| indicate extreme asymmetry, the lowest values indicate normality, and the values of between |8.00| and |20.00| suggest extreme kurtosis [ 44 ]). The results of metacognitive skills on the ACRAr subscale in both groups were acceptable for both indicators (see Table 3 ). Therefore, parametric statistics were used. Descriptive statistics are also shown in Table A1 and Table A2 (see Appendix A ).

Indicators of asymmetry and kurtosis in the experimental group and control group.

Note. M = Mean Age; SD = Standard Deviation; A = Asymmetry ; K = Kurtosis; ASE = Asymmetry Standard Error; SEK = Kurtosis Standard Error.

3.2. Previous Statistical Analysis of Homogeneity between the Groups before the Intervention

Significant differences between both groups (experimental and control) in their use of metacognitive strategies were anlayzed before application of the different types of blended learning (Type 1 vs. Type 2). To do so, a single-factor ANOVA with fixed-effects was performed (blended learning type) on the results. No significant differences were found between both, so they can be considered similar groups (F 1 , 119 = 0.276; p = 0.601; η 2 = 0.002) in the ACRAr subscale of metacognitive skills.

Similarly, in order to study which type of supervised learning technique would be the most appropriate, the Goodness-of-fit indices were measured in the structural equation modeling (SEM) that was used to study the settings of the machine learning technique to predict the learning results. The calculations were performed with the Statistical Package for the Social Sciences (SPSS) AMOS v.24, as may be seen in Table 4 , and no dependent relations between the observed values and the different prediction methods (LR, DT, RBFN, and kNN) were found for any of the four prediction models. Among these possibilities, the following were applied in the MRA.

Goodness-of-fit indices.

Note. df = degrees of liberty; χ 2 = Chi squared; LR = Linear Regression; DT = Decision Trees; RBFN = Radial basis function network; kNN = k-Nearest Neighbor classification; NFI = normed-fit-index; RMSEA = Root-Mean-Square Error of Approximation; SRMR = Standardized Root-Mean-Square Residual; TLI = Tucker–Lewis index; CFI = comparative fit index; AIC = Akaike Information criterion; ECVI = parsimony index.

3.3. Hypothesis 1.

MRA was performed to study the predictive value of the variable blended learning type applied to the student learning outcomes. An R 2 = 0.404 was found, which indicates that this variable explained 40.04% of the variance in the learning results. The Tolerance ( T) values were within an interval of 0.106 and 0.336 and the Variance Inflation Factor ( VIF ) between 3.491 and 9.45, so none of the variables had to be removed. Likewise, the highest partial correlation was found in the Learning Outcomes Total ( r = 0.586; p = 0.000), see Table A3 .

3.4. Hypothesis 2.

MRA yielded a figure of R 2 = 0.719 in the study of the predictive value of blended learning applied to student behaviors on the platform. This figure indicated that the blended learning type in use explained 71.19% of the variance in the learning behaviors of students on the platform. The Tolerance (T) values were situated within an interval between 0.136 and 0.539 and the Variance Inflation Factor (VIF) between 1.472 and 7.346, so that no variable had to be removed. The highest partial correlation was found in Access to Teacher Feedback ( r = 0.448), see Table A4 .

3.5. Hypothesis 3.

A k-means clustering technique was applied in each type of blended learning in use (Blended Learning type 1 vs. Blended Learning type 2), as seen in Table 5 . Three clusters are shown in Table 5 that were found in the two types of blended learning (Cluster 1, Sufficient; Cluster 2, Intermediary; and Cluster 3, Excellent. The classification of Cluster type was according to the maximum possible value in each learning outcome and number of accesses obtained). Higher values for performance were found in the Blended Learning type 1 rather than the Blended Learning type 2 in all three clusters, specifically in Learning Outcomes Total. Likewise, with regard to the learning behaviors developed by students in the type of blended learning in use (Blended Learning type 1 vs. Blended Learning type 2), as may be seen in Table 5 , a higher number of log-ons to the platform in the Blended Learning type 1 rather than the Blended Learning type 2 environment were found, except for student queries on theoretical information provided by the teacher (see Table 6 ).

Centers of final clusters for the learning results variable in Blended Learning type 1 and type 2, Blended Learning type 1: a n = 1; b n = 45; c n = 17; Blended Learning type 2: A lost value is observed. a n = 9; b n = 30; c n = 18

Note. PBLD = Project-Based Learning Development; PBLE = Project-Based Learning Exhibition.

Centers of final clusters and the variable behavioral learning on the LMS in Blended Learning type 1 and type 2. Blended Learning type 1: a lost value is observed. a n = 31; b n = 27; c n = 5; Blended Learning type 2: Two lost values were observed. a n = 36; b n = 16; c n = 6.

Note; PBL = Project-Based Learning Development.

Figure 1 shows the scores in the two groups: experimental group (red color) and control group (blue color). As can be seen, there was a greater homogeneity of higher scores in the experimental group for different types of Learning outcomes. Similarly, Figure 2 points to the distributions of LMS behavioral learning scores in different resources.

Distribution of scores in the different types of Learning outcomes. Note. Development PBL = Development Project-Based Learning outcomes; Exhibition PBL = Exhibition Project-Based Learning outcomes; Test = Test Learning outcomes; Total LO = Learning outcomes Total.

Distribution of scores in the different types of behavioral learning in the LMS. Note. CI = Access to Complementary Information scores; CPBL = Access to guidance to prepare PBL scores; TI = Access to Theoretical Information scores; F = Access to Teacher Feedback; MVD = Mean Visits per day.

4. Discussion

In the blended learning environments, the type of teaching design appears to be a predictive factor in both the learning results and the learning behaviors that the students develop in the LMS. blended learning with 80% of interactions in LMS appeared to be more effective, both with respect to the learning results of the students and the effectiveness of the learning behaviors that they develop. This type of pedagogic design includes the use of hypermedia resources that strengthen teacher feedback in real time, which furthers the development of SRL strategies [ 24 , 35 ]. This aspect is of special relevance for teachers in nursing higher education, and the implicit message is that they would be well advised to design their materials for use in a blended learning environment [ 27 , 28 , 29 , 30 ], as those environments appear to have increased the effectiveness of active methodologies, especially PBL with hypermedia resources in LMS [ 38 ]. In addition, Blended Learning type 1 (80% the interaction in the LMS) strengthens students’ use of learning-based projects that have been considered more effective in the LMS [ 2 , 23 ]. These behaviors range from access to feedback given by the teacher to tasks carried out by the student or the collaborative groups and the average number of visits per day [ 23 ]. All of this indicates that the Blended Learning type 1 design increases the interaction of the student in the LMS and that interaction also facilitates student access to feedback from the teacher, as the LMS can be consulted as many times as necessary when learning, an aspect that is less feasible with F2F instruction [ 9 , 10 , 18 ]. In this way, the teachers can structure their help and prepare specific materials for each group.

In addition, machine learning techniques have been used in this study, in view of their effective use with what is known as data mining [ 13 , 18 , 36 ]. In particular, supervised and unsupervised machine learning techniques have been used (linear regression and clustering k-means methods, respectively). Prediction and clustering studies, among others, can be conducted with these techniques, which help the teacher to gain knowledge of the learning characteristics of students and to predict at-risk students [ 23 , 34 , 38 ]. Even so, it is true that these techniques should be used throughout the whole teaching process to be able to develop personalized actions for student learning [ 35 , 36 ]. In subsequent studies, therefore, development of the learning process among students at the start, in the middle, and at the end of the study module will be analyzed with machine learning techniques [ 12 , 13 ].

5. Limitations

This study has limitations, but the results of this study should, nevertheless, be given prudent consideration. Limitations include the following: methodological intervention was in one university, the students were from a specific country, convenience sampling was applied, the knowledge area of the students was specific, and the type of design (quasi-experimental) was also specific. Although, it must be taken into consideration that there are few specific studies to test the effectiveness of this type of methodology in nursing students. Studies that have been carried out have similar characteristics that are justified from the specificity of this research [ 28 , 29 , 30 ].

Therefore, future studies will be directed at increasing the size of the sample and the diversity of the nursing degree course level. Therefore, this profession is subject to continuous theoretical and technological advances that require systematic research on how to teach better in order to learn more effectively.

6. Conclusions

This research study has identified the characteristics to design an effective LMS in the nursing degree. The use of prediction and clustering techniques is very important to facilitate personalized learning and to analyze how resources are better utilized in the blended learning space. This type of analysis can be automatically generated in LMS environments, such as Moodle, and could be integrated in modules and plugins. These tools would facilitate rapid and straightforward generation of those analyses, which would be of great utility for the teacher and would assist with the early detection of at-risk students, as well as behavioral analyses of both the individual student and the collaborative groups of students, which would foreseeably increase the teaching quality and learning outcomes. This need has been underlined in such studies as those by Peña-Ayala [ 12 ] and Romero et al. [ 17 ], and they have to be approved by university management, but they will virtually be a necessity in 21st century teaching as we move closer to personalized on-line teaching, both in F2F teaching and in virtual learning environments. In summary, this teaching design is especially significant in the nursing degree since project work is a practice that has proved very effective in the training of future professionals.

Good results have been obtained in all assessment tests in the two types of blended learning. However, the type of blended learning that applied automated feedback and hypermedia resources obtained even better results (more percentage of work in the LMS) [ 28 , 29 , 30 ]. One explanation may be that the student can access information in the LMS at any time, which is not possible for F2F interaction, and this facilitates personalization of learning and motivates the student [ 7 , 8 , 39 ]. Therefore, incorporating these forms of work in teaching in the field of health is a very effective option.

The results obtained are in line with those found in the research of Oh & Lee [ 28 ]. The use of PBL methodology in blended learning environments empowers nursing students to acquire practical skills that are of great help for nursing work in real intervention environments [ 28 ]. This form of teaching is flexible [ 30 ] because it facilitates development and tests hypotheses in the resolution of tasks similar to those they will encounter in a working environment, and in addition, group work facilitates the acquisition of collaborative work skills, which they will also encounter in such working environments. All this increases the self-efficacy and critical thinking skills of these professionals. Recent studies recommend the application of this methodology within the nursing degree curricula [ 27 ].

In sum, it can be concluded that this way of teaching seems to be effective for nursing students. Although, more studies are needed in this field aimed at studying the effectiveness of blended learning in teaching in the nursing degree.

Acknowledgments

Thanks to all the students who participated in this study and the Committee of Bioethics of University of Burgos (Spain).

Supplementary Materials

The following are available online at https://www.mdpi.com/1660-4601/17/5/1589/s1 , Table S1: Rubric to evaluate development of Project-Based Learning, Table S2: Rubric to evaluate Exhibition of Project-Based Learning.

Descriptive statistics for the Learning outcomes.

Note: M = Mean; SD = Standard Deviation; PBL = Project-Based Learning; α = Reliability index of Cronbach’s Alpha.

Descriptive statistics for Behavioral learning in the LMS.

Note: M = Mean; SD = Standard Deviation

Coefficients in the prediction of learning outcomes with variable types of Blended Learning.

Note: Dependent variable: Blended Learning type; PBL = Project-Based Learning; VIF = Variance Inflation Factor.

Coefficients in the prediction of Behavioral learning with variable types of Blended Learning.

Note: Dependent variable: Blended Learning type; VIF = Variance Inflation Factor.

Author Contributions

Conceptualization, M.C.S.-M., and M.-C.E.-L.; methodology, M.C.S.-M.; software, Á.A.G.; validation, M.C.S.-M., and Á.A.G.; formal analysis, M.C.S.-M.; investigation, M.C.S.-M., and M.-C.E.-L.; resources, M.C.S.-M.; data curation, M.C.S.-M.; writing—original draft preparation, M.C.S.-M., and M.-C.E.-L. and Á.A.G.; writing—review and editing, M.C.S.-M., and M.-C.E.-L. and Á.A.G.; visualization, Á.A.G.; supervision, M.C.S.-M., and M.-C.E.-L.; project administration, M.C.S.-M.; funding acquisition, M.C.S.-M., and M.-C.E.-L. All authors have read and agreed to the published version of the manuscript.

This research was funded by the of Consejería de Educación de la Junta de Castilla y León (Spain) (Department of Education of the Junta de Castilla y León), Grant number BU032G19, and grants from the University of Burgos for the dissemination and the improvement of teaching innovation experiences of the Vice-Rectorate of Teaching and Research Staff, the Vice-Rectorate for Research and Knowledge Transfer, 2020, at the University of Burgos (Spain).

Conflicts of Interest

The authors declare no conflicts of interest.