a research paper on education

• 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.

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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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Education Technology: An Evidence-Based Review

In recent years, there has been widespread excitement around the potential for technology to transform learning. As investments in education technology continue to grow, students, parents, and teachers face a seemingly endless array of education technologies from which to choose—from digital personalized learning platforms to educational games to online courses. Amidst the excitement, it is important to step back and understand how technology can help—or in some cases hinder—how students learn. This review paper synthesizes and discusses experimental evidence on the effectiveness of technology-based approaches in education and outlines areas for future inquiry. In particular, we examine RCTs across the following categories of education technology: (1) access to technology, (2) computer-assisted learning, (3) technology-enabled behavioral interventions in education, and (4) online learning. While this review focuses on literature from developed countries, it also draws upon extensive research from developing countries. We hope this literature review will advance the knowledge base of how technology can be used to support education, outline key areas for new experimental research, and help drive improvements to the policies, programs, and structures that contribute to successful teaching and learning.

We are extremely grateful to Caitlin Anzelone, Rekha Balu, Peter Bergman, Brad Bernatek, Ben Castleman, Luke Crowley, Angela Duckworth, Jonathan Guryan, Alex Haslam, Andrew Ho, Ben Jones, Matthew Kraft, Kory Kroft, David Laibson, Susanna Loeb, Andrew Magliozzi, Ignacio Martinez, Susan Mayer, Steve Mintz, Piotr Mitros, Lindsay Page, Amanda Pallais, John Pane, Justin Reich, Jonah Rockoff, Sylvi Rzepka, Kirby Smith, and Oscar Sweeten-Lopez for providing helpful and detailed comments as we put together this review. We also thank Rachel Glennerster for detailed support throughout the project, Jessica Mardo and Sophie Shank for edits, and to the Spencer Foundation for financial support. Any errors or omissions are our own. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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The Impact of COVID-19 on Education: A Meta-Narrative Review

Aras bozkurt.

1 Distance Education Department, Anadolu University, Eskişehir, Turkey

2 Department of English Studies, University of South Africa, Pretoria, South Africa

3 Anadolu Üniversitesi, Açıköğretim Fakültesi, Kat:7, Oda:702, 26470, Tepebaşı, Eskişehir, Turkey

Kadir Karakaya

4 Applied Linguistics & Technology Department, Iowa State University, Ames, IA USA

5 Educational Psychology, Learning Sciences, University of Oklahoma, Norman, OK USA

Özlem Karakaya

6 Educational Technology & Human-Computer Interaction, Iowa State University, Ames, IA USA

Daniela Castellanos-Reyes

7 Curriculum and Instruction, Learning Design and Technology, Purdue University, West Lafayette, IN USA

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The dataset is available from the authors upon request.

The rapid and unexpected onset of the COVID-19 global pandemic has generated a great degree of uncertainty about the future of education and has required teachers and students alike to adapt to a new normal to survive in the new educational ecology. Through this experience of the new educational ecology, educators have learned many lessons, including how to navigate through uncertainty by recognizing their strengths and vulnerabilities. In this context, the aim of this study is to conduct a bibliometric analysis of the publications covering COVID-19 and education to analyze the impact of the pandemic by applying the data mining and analytics techniques of social network analysis and text-mining. From the abstract, title, and keyword analysis of a total of 1150 publications, seven themes were identified: (1) the great reset, (2) shifting educational landscape and emerging educational roles (3) digital pedagogy, (4) emergency remote education, (5) pedagogy of care, (6) social equity, equality, and injustice, and (7) future of education. Moreover, from the citation analysis, two thematic clusters emerged: (1) educational response, emergency remote education affordances, and continuity of education, and (2) psychological impact of COVID-19. The overlap between themes and thematic clusters revealed researchers’ emphasis on guaranteeing continuity of education and supporting the socio-emotional needs of learners. From the results of the study, it is clear that there is a heightened need to develop effective strategies to ensure the continuity of education in the future, and that it is critical to proactively respond to such crises through resilience and flexibility.

Introduction

The Coronavirus (COVID-19) pandemic has proven to be a massive challenge for the entire world, imposing a radical transformation in many areas of life, including education. It was rapid and unexpected; the world was unprepared and hit hard. The virus is highly contagious, having a pathogenic nature whose effects have not been limited to humans alone, but rather, includes every construct and domain of societies, including education. The education system, which has been affected at all levels, has been required to respond to the crisis, forced to transition into emergency modes, and adapt to the unprecedented impact of the global crisis. Although the beginning of 2021 will mark nearly a year of experience in living through the pandemic, the crisis remains a phenomenon with many unknowns. A deeper and more comprehensive understanding of the changes that have been made in response to the crisis is needed to survive in these hard times. Hence, this study aims to provide a better understanding by examining the scholarly publications on COVID-19 and education. In doing this, we can identify our weaknesses and vulnerabilities, be better prepared for the new normal, and be more fit to survive.

Related Literature

Though the COVID-19 pandemic is not the first major disruption to be experienced in the history of the world, it has been unique due to its scale and the requirements that have been imposed because of it (Guitton, 2020 ). The economies of many countries have greatly suffered from the lockdowns and other restrictive measurements, and people have had to adapt to a new lifestyle, where their primary concern is to survive by keeping themselves safe from contracting the deadly virus. The education system has not been exempt from this series of unfortunate events inflicted by COVID-19. Since brick-and-mortar schools had to be closed due to the pandemic, millions of students, from those in K-12 to those in higher education, were deprived of physical access to their classrooms, peers, and teachers (Bozkurt & Sharma, 2020a , b ). This extraordinary pandemic period has posed arguably the most challenging and complex problems ever for educators, students, schools, educational institutions, parents, governments, and all other educational stakeholders. The closing of brick-and-mortar schools and campuses rendered online teaching and learning the only viable solution to the problem of access-to-education during this emergency period (Hodges et al., 2020 ). Due to the urgency of this move, teachers and instructors were rushed to shift all their face-to-face instruction and instructional materials to online spaces, such as learning management systems or electronic platforms, in order to facilitate teaching virtually at a distance. As a result of this sudden migration to learning and instruction online, the key distinctions between online education and education delivered online during such crisis and emergency circumstances have been obfuscated (Hodges et al., 2020 ).

State of the Current Relevant Literature

Although the scale of the impact of the COVID-19 global pandemic on education overshadows previously experienced nationwide or global crises or disruptions, the phenomenon of schools and higher education institutions having to shift their instruction to online spaces is not totally new to the education community and academia (Johnson et al., 2020 ). Prior literature on this subject indicates that in the past, schools and institutions resorted to online or electronic delivery of instruction in times of serious crises and uncertainties, including but not limited to natural disasters such as floods or earthquakes (e.g., Ayebi-Arthur, 2017 ; Lorenzo, 2008 ; Tull et al., 2017 ), local disruptions such as civil wars and socio-economic events such as political upheavals, social turmoils or economic recessions (e.g., Czerniewicz et al., 2019 ). Nevertheless, the past attempts to move learning and teaching online do not compare to the current efforts that have been implemented during the global COVID-19 pandemic, insofar as the past crisis situations were sporadic events in specific territories, affecting a limited population for relatively short periods of time. In contrast, the COVID-19 pandemic has continued to pose a serious threat to the continuity of education around the globe (Johnson et al., 2020 ).

Considering the scale and severity of the global pandemic, the impacts it has had on education in general and higher education in particular need to be explored and studied empirically so that necessary plans and strategies aimed at reducing its devastating effects can be developed and implemented. Due to the rapid onset and spread of the global pandemic, the current literature on the impact of COVID-19 on education is still limited, including mostly non-academic editorials or non-empirical personal reflections, anecdotes, reports, and stories (e.g., Baker, 2020 ; DePietro, 2020 ). Yet, with that said, empirical research on the impact of the global pandemic on higher education is rapidly growing. For example, Johnson et al. ( 2020 ), in their empirical study, found that faculty members who were struggling with various challenges adopted new instructional methods and strategies and adjusted certain course components to foster emergency remote education (ERE). Unger and Meiran ( 2020 ) observed that the pandemic made students in the US feel anxious about completing online learning tasks. In contrast, Suleri ( 2020 ) reported that a large majority of European higher education students were satisfied with their virtual learning experiences during the pandemic, and that most were willing to continue virtual higher education even after the pandemic (Suleri, 2020 ). The limited empirical research also points to the need for systematically planning and designing online learning experiences in advance in preparation for future outbreaks of such global pandemics and other crises (e.g., Korkmaz & Toraman, 2020 ). Despite the growing literature, the studies provide only fragmentary evidence on the impact of the pandemic on online learning and teaching. For a more thorough understanding of the serious implications the pandemic has for higher education in relation to learning and teaching online, more empirical research is needed.

Unlike previously conducted bibliometric analysis studies on this subject, which have largely involved general analysis of research on health sciences and COVID-19, Aristovnik et al. ( 2020 ) performed an in-depth bibliometric analysis of various science and social science research disciplines by examining a comprehensive database of document and source information. By the final phase of their bibliometric analysis, the authors had analyzed 16,866 documents. They utilized a mix of innovative bibliometric approaches to capture the existing research and assess the state of COVID-19 research across different research landscapes (e.g., health sciences, life sciences, physical sciences, social sciences, and humanities). Their findings showed that most COVID-19 research has been performed in the field of health sciences, followed by life sciences, physical sciences, and social sciences and humanities. Results from the keyword co-occurrence analysis revealed that health sciences research on COVID-19 tended to focus on health consequences, whereas the life sciences research on the subject tended to focus on drug efficiency. Moreover, physical sciences research tended to focus on environmental consequences, and social sciences and humanities research was largely oriented towards socio-economic consequences.

Similarly, Rodrigues et al. ( 2020 ) carried out a bibliometric analysis of COVID-19 related studies from a management perspective in order to elucidate how scientific research and education arrive at solutions to the pandemic crisis and the post-COVID-19 era. In line with Aristovnik et al.’s ( 2020 ) findings, Rodrigues et al. ( 2020 ) reported that most of the published research on this subject has fallen under the field of health sciences, leaving education as an under-researched area of inquiry. The content analysis they performed in their study also found a special emphasis on qualitative research. The descriptive and content analysis yielded two major strands of studies: (1) online education and (2) COVID-19 and education, business, economics, and management. The online education strand focused on the issue of technological anxiety caused by online classes, the feeling of belonging to an academic community, and feedback.

Lastly, Bond ( 2020 ) conducted a rapid review of K-12 research undertaken in the first seven months of the COVID-19 pandemic to identify successes and challenges and to offer recommendations for the future. From a search of K-12 research on the Web of Science, Scopus, EBSCOHost, the Microsoft Academic, and the COVID-19 living systematic map, 90 studies were identified and analyzed. The findings revealed that the reviewed research has focused predominantly on the challenges to shifting to ERE, teacher digital competencies and digital infrastructure, teacher ICT skills, parent engagement in learning, and students’ health and well-being. The review highlighted the need for straightforward communication between schools and families to inform families about learning activities and to promote interactivity between students. Teachers were also encouraged to develop their professional networks to increase motivation and support amongst themselves and to include opportunities for both synchronous and asynchronous interaction for promoting student engagement when using technology. Bond ( 2020 ) reported that the reviewed studies called for providing teachers with opportunities to further develop their digital technical competencies and their distance and online learning pedagogies. In a recent study that examines the impact of COVID-19 at higher education (Bozkurt, 2022 ), three broad themes from the body of research on this subject: (1) educational crisis and higher education in the new normal: resilience, adaptability, and sustainability, (2) psychological pressures, social uncertainty, and mental well-being of learners, and (3) the rise of online distance education and blended-hybrid modes. The findings of this study are similar to Mishra et al. ( 2021 ) who examined the COVID-19 pandemic from the lens of online distance education and noted that technologies for teaching and learning and psychosocial issues were emerging issues.

The aforementioned studies indicate that a great majority of research on COVID-19 has been produced in the field of health sciences, as expected. These studies nonetheless note that there is a noticeable shortage of studies dealing with the effects of the pandemic in the fields of social sciences, humanities, and education. Given the profound impact of the pandemic on learning and teaching, as well as on the related stakeholders in education, now more than ever, a greater amount of research on COVID-19 needs to be conducted in the field of education. The bibliometric studies discussed above have analyzed COVID-19 research across various fields, yielding a comparative snapshot of the research undertaken so far in different research spheres. However, despite being comprehensive, these studies did not appear to have examined a specific discipline or area of research in depth. Therefore, this bibliometric study aims to provide a focused, in-depth analysis of the COVID-19-related research in the field of education. In this regard, the main purpose of this study is to identify research patterns and trends in the field of education by examining COVID-19-related research papers. The study sought to answer the following research questions:

• What are the thematic patterns in the title, abstract, and keywords of the publications on COVID-19 and education?
• What are the citation trends in the references of the sampled publications on COVID-19 and education?

Methodology

This study used data mining and analytic approaches (Fayyad et al., 2002 ) to examine bibliometric patterns and trends. More specifically, social network analysis (SNA) (Hansen et al., 2020 ) was applied to examine the keywords and references, while text-mining was applied (Aggarwal & Zhai, 2012 ) to examine the titles and abstracts of the research corpus. Keywords represent the essence of an article at a micro level and for the analysis of the keywords, SNA was used. SNA “provides powerful ways to summarize networks and identify key people, [entities], or other objects that occupy strategic locations and positions within a matrix of links” (Hansen et al., 2020 , p. 6). In this regard, the keywords were analyzed based on their co-occurrences and visualized on a network graph by identifying the significant keywords which were demonstrated as nodes and their relationships were demonstrated with ties. For text-mining of the titles and abstracts, the researchers performed a lexical analysis that employs “two stages of co-occurrence information extraction—semantic and relational—using a different algorithm for each stage” (Smith & Humphreys, 2006 , p. 262). Thus, text-mining analysis enabled researchers to identify the hidden patterns and visualize them on a thematic concept map. For the analysis of the references, the researchers further used SNA based on the arguments that “citing articles and cited articles are linked to each other through invisible ties, and they collaboratively and collectively build an intellectual community that can be referred to as a living network, structure, or an ecology” (Bozkurt, 2019 , p. 498). The analysis of the references enabled the researchers to identify pivotal scholarly contributions that guided and shaped the intellectual landscape. The use of multiple approaches enables the study to present a broader view, or a meta-narrative.

Sample and Inclusion Criteria

The publications included in this research met the following inclusion criteria: (1) indexed by the Scopus database, (2) written in English, and (3) had the search queries on their title (Table ​ (Table1). 1 ). The search query reflects the focus on the impact of COVID-19 on education by including common words in the field like learn , teach , or student . Truncation was also used in the search to capture all relevant literature. Narrowing down the search allowed us to exclude publications that were not education related. Scopus was selected because it is one of the largest scholarly databases, and only publications in English were selected to facilitate identification of meaningful lexical patterns through text-mining and provide a condensed view of the research. The search yielded a total of 1150 papers (articles = 887, editorials = 66, notes = 58, conference papers = 56, letters = 40, review studies = 30, book chapters = 9, short surveys = 3, books = 1).

Search strings used to create research corpus

Data Analysis and Research Procedures

This study has two phases of analysis. In the first phase, text mining was used to analyze titles and abstracts, and SNA was applied to analyze keywords. By using two different analytical approaches, the authors were able to triangulate the research findings (Thurmond, 2001 ). In this phase, using lexical algorithms, text mining analysis enabled visualizing the textual data on a thematic concept map according to semantic relationships and co-occurrences of the words (Fig.  1 ). Text mining generated a machine-based concept map by analyzing the co-occurrences and lexical relationships of textual data. Then, based on the co-occurrences and centrality metrics, SNA enabled visualizing keywords on a network graphic called sociogram (Fig.  2 ). SNA allowed researchers to visually identify the key terms on a connected network graph where keywords are represented as nodes and their relationships are represented as edges. In the first phase of the study, by synthesizing outputs of the data mining and analytic approaches, meaningful patterns of textual data were presented as seven main research themes.

Thematic concept mapping of COVID-19 and education-related papers

Social networks analysis of the keywords in COVID-19 and education-related papers

In the second phase of the study, through the examination of the references and citation patterns (e.g., citing and being cited) of the articles in the research corpus, the citation patterns were visualized on a network graphic by clusters (See Fig.  3 ) showing also chronical relationships which enabled to identify pivotal COVID-19 studies. In the second phase of the study, two new themes were identified which were in line with the themes that emerged in the first phase of the study.

Social networks analysis of the references in COVID-19 and education-related papers 2019–2020 (Only the first authors were labeled – See Appendix Fig. ​ Fig.4 4 for SNA of references covering pre-COVID-19 period)

Strengths and Limitations

This study is one of the first attempts to use bibliometric approaches benefiting from data mining and analysis techniques to better understand COVID-19 and its consequences on published educational research. By applying such an approach, a large volume of data is able to be visualized and reported. However, besides these strengths, the study also has certain limitations. First, the study uses the Scopus database, which, though being one of the largest databases, does not include all types of publications. Therefore, the publications selected for this study offer only a partial view, as there are many significant publications in gray literature (e.g., reports, briefs, blogs). Second, the study includes only publications written in English, however, with COVID-19 being a global crisis, publications in different languages would provide a complementary view and be helpful in understanding local reflections in the field of education.

Findings and Discussion

Sna and text-mining: thematic patterns in the title, abstract, and keywords of the publications.

This section reports the findings based on a thematic concept map and network graphic that were developed through text mining (Fig.  1 —Textual data composed of 186.234 words visualized according to lexical relationships and co-occurrences) and sociograms created using SNA (Fig.  2 —The top 200 keywords with highest betweenness centrality and 1577 connections among them mapped on a network graph) to visualize the data. Accordingly, seven major themes were identified by analyzing the data through text-mining and SNA: (1) the great reset, (2) digital pedagogy, (3) shifting educational landscape and emerging educational roles, (4) emergency remote education, (5) pedagogy of care, (6) social equity, equality, and injustice, and (7) future of education.

• Theme 1: The Great Reset (See path Fig.  1 : lockdown  +  emergency  +  community  +  challenges  +  during  >  pandemic and impact  >  outbreak  >  coronavirus  >  pandemic and global  >  crisis  >  pandemic  >  world; See nodes on Fig.  2 : Covid19, pandemic, Coronavirus, lockdown, crisis ). The first theme in the thematic concept map and network graphic is the Great Reset. It has been relatively a short time since the World Health Organization (WHO) declared the COVID-19 a pandemic. Although vaccination had already started, the pandemic continued to have an adverse impact on the world. Ever since the start of the pandemic, people were discussing when there would be a return to normal (Bozkurt & Sharma, 2020a , b ; Xiao, 2021 ); however, as time goes by, this hope has faded, and returning to normal appears to be far into the future (Schwab & Malleret, 2020 ). The pandemic is seen as a major milestone, in the sense that a macro reset in economic, social, geopolitical, environmental, and technological fields will produce multi-faceted changes affecting almost all aspects of life (Schwab & Malleret, 2020 ). The cover of an issue of the international edition of Time Magazine reflected this idea of a great reset and presented the COVID-19 pandemic as an opportunity to transform the way we live and work (Time, 2020 ). It has been argued that the pandemic will generate the emergence of a new era, and that we will have to adapt to the changes it produces (Bozkurt & Sharma, 2020 ). For example, the industrial sector quickly embraced remote work despite its challenges, and it is possible that most industrial companies will not return to the on-site working model even after the pandemic ends (Hern, 2020 ). We can expect a high rate of similar responses in other fields, including education, where COVID-19 has already reshaped our educational systems, the way we deliver education, and pedagogical approaches.
• Theme 2: Digital pedagogy (See path on Fig.  1 : distance learning  >  research  >  teacher  >  development  >  need  >  training  +  technology  +  virtual  >  digital  >  communication  >  support  >  process  >  teaching  >  online  >  learning  >  online learning  +  course  >  faculty  >  students  >  experience ; See nodes on Fig.  2 : online learning, distance learning, computer-based learning, elearning, online education, distance education, online teaching, multimedia-based learning, technology, blended learning, online, digital transformation, ICT, online classes, flexible learning, technology-enhanced learning, digitalization ). Owing to the rapid transition to online education as a result of COVID-19, digital pedagogy and teachers’ competencies in information and communication technology (ICT) integration have gained greater prominence with the unprecedented challenges teachers have faced to adapt to remote teaching and learning. The COVID-19 pandemic has unquestionably manifested the need to prepare teachers to teach online, as most of them have been forced to assume ERE roles with inadequate preparation. Studies involving the use of SNA indicate a correspondence between adapting to a digital pedagogy and the need to equip teachers with greater competency in technology and online teaching (e.g., Blume, 2020 ; König et al., 2020 ). König et al. ( 2020 ) conducted a survey-based study investigating how early career teachers have adapted to online teaching during COVID-19 school closures. Their study found that while all the teachers maintained communication with students and their parents, introduced new learning content, and provided feedback, they lacked the ability to respond to challenges requiring ICT integration, such as those related to providing quality online teaching and to conducting assessments. Likewise, Blume ( 2020 ) noted that most teachers need to acquire digital skills to implement digitally-mediated pedagogy and communication more effectively. Both study findings point to the need for building ICT-related teaching and learning competencies in initial teacher education and teacher professional development. The findings from the SNA conducted in the present study are in line with the aforementioned findings in terms of keyword analysis and overlapping themes and nodes.
• Theme 3: Shifting educational landscape and emerging educational roles (See path on Fig.  1 : future > education > role > Covid19; See nodes on Fig.  2 : higher education, education, student, curriculum, university, teachers, learning, professional development, teacher education, knowledge, readiness ). The role of technology in education and human learning has been essential during the COVID-19 pandemic. Technology has become a prerequisite for learning and teaching during the pandemic and will likely continue to be so after it. In the rapid shift to an unprecedented mode of learning and teaching, stakeholders have had to assume different roles in the educational landscape of the new normal. For example, in a comprehensive study involving the participation of over 30 K higher education students from 62 countries conducted by Aristovnik et al. ( 2020 ), it was found that students with certain socio-demographic characteristics (male, lower living standard, from Africa or Asia) were significantly less satisfied with the changes to work/life balance created by the COVID-19 pandemic, and that female students who were facing financial problems were generally more affected by COVID-19 in their emotional life and personal circumstances. Despite the challenges posed by the pandemic, there is likely to be carry over in the post-pandemic era of some of the educational changes made during the COVID-19 times. For example, traditional lecture-based teacher-centered classes may be replaced by more student-centered online collaborative classes (Zhu & Liu, 2020 ). This may require the development and proliferation of open educational platforms that allow access to high-quality educational materials (Bozkurt et al., 2020 ) and the adoption of new roles to survive in the learning ecologies informed by digital learning pedagogies. In common with the present study, the aforementioned studies (e.g., Aristovnik et al., 2020 ; König et al., 2020 ) call for more deliberate actions to improve teacher education programs by offering training on various teaching approaches, such as blended, hybrid, flexible, and online learning, to better prepare educators for emerging roles in the post-pandemic era.
• Theme 4: Emergency remote education (see path Fig.  1 : higher education  >  university  >  student  >  experience  >  remote; See nodes on Fig.  2 : Covid19, pandemic, Coronavirus, higher education, education, school closure, emergency remote teaching, emergency remote learning ). Educational institutions have undergone a rapid shift to ERE in the wake of COVID-19 (Bozkurt & Sharma, 2020a ; Bozkurt et al., 2020 ; Hodges et al., 2020 ). Although ERE is viewed as similar to distance education, they are essentially different. That is, ERE is a prompt response measure to an emergency situation or unusual circumstances, such as a global pandemic or a civil war, for a temporary period of time, whereas distance education is a planned and systematic approach to instructional design and development grounded in educational theory and practice (Bozkurt & Sharma, 2020b ). Due to the urgent nature of situations requiring ERE, it may fall short in embracing the solid pedagogical learning and teaching principles represented by distance education (Hodges et al., 2020 ). The early implementations of ERE primarily involved synchronous video-conferencing sessions that sought to imitate in-person classroom instruction. It is worth noting that educators may have heavily relied on synchronous communication to overcome certain challenges, such as the lack of available materials and planned activities for asynchronous communication. Lockdowns and school closures, which turned homes into compulsory learning environments, have posed major challenges for families and students, including scheduling, device sharing, and learner engagement in a socially distanced home learning environment (Bond, 2020 ). For example, Shim and Lee ( 2020 ) conducted a qualitative study exploring university students’ ERE experiences and reported that students complained about network instability, unilateral interactions, and reduced levels of concentration. The SNA findings clearly highlight that there has been a focus on ERE due to the school closures during the COVID-19 pandemic. It is key to adopt the best practices of ERE and to utilize them regularly in distance education (Bozkurt, 2022 ). Moreover, it is important to note that unless clear distinctions are drawn between these two different forms of distance education or virtual instruction, a series of unfortunate events in education during these COVID-19 times is very likely to take place and lead to fatal errors in instructional practices and to poor student learning outcomes.
• Theme 5: Pedagogy of care (See path Fig.  1 : r ole  >  education  >  Covid19  >  care ; See nodes on Fig.  2 : Stress, anxiety, student wellbeing, coping, care, crisis management, depression ). The thematic concept map and network graphic show the psychological and emotional impact of the COVID-19 pandemic on various stakeholders, revealing that they have experienced anxiety, expressed the need for care, and sought coping strategies. A study by Baloran ( 2020 ), conducted in the southern part of the Philippines to examine college students’ knowledge, attitudes, anxiety, and personal coping strategies during the COVID-19 pandemic, found that the majority of the students experienced anxiety during the lockdown and worried about food security, financial resources, social contact, and large gatherings. It was reported that the students coped with this anxiety by following protective measures, chatting with family members and friends, and motivating themselves to have a positive attitude. In a similar study, Islam et al. ( 2020 ) conducted an investigation to determine whether Bangladeshi college students experienced anxiety and depression and the factors responsible for these emotional responses. Their cross-sectional survey-based study found that a large percentage of the participants had suffered from anxiety and depression during the pandemic. Academic and professional uncertainty, as well as financial insecurity, have been documented as factors contributing to the anxiety and depression among college students. Both studies point to the need for support mechanisms to be established by higher education institutions in order to ensure student wellbeing, provide them with care, and help them to cope with stress, anxiety, and depression. Talidong and Toquero ( 2020 ) reported that, in addition to students’ well-being and care, teachers’ perceptions and experiences of stress and anxiety during the quarantine period need to be taken into account. The authors found that teachers were worried about the safety of their loved ones and were susceptible to anxiety but tended to follow the preventive policies. A pedagogy of care has been presented as an approach that would effectively allow educators to plan more supportive teaching practices during the pandemic by fostering clear and prompt communication with students and their families and taking into consideration learner needs in lesson planning (e.g., Karakaya, 2021 ; Robinson et al., 2020 ). Here it is important to stress that a pedagogy of care is a multifaceted concept, one that involves the concepts of social equity, equality, and injustice.
• Theme 6: Social equity, equality, and injustice (See path on Fig.  1 : Impact  >  outbreak  >  coronavirus  >  pandemic  >  social ; See nodes on Fig.  2 : Support, equity, social justice, digital divide, inequality, social support ). One of the more significant impacts of COVID-19 has been the deepening of the existing social injustices around the world (Oldekop et al., 2020 ; Williamson et al., 2020 ). Long-term school closures have deteriorated social bonds and adversely affected health issues, poverty, economy, food insecurity, and digital divide (Van Lancker & Parolin, 2020 ). Regarding the digital divide, there has been a major disparity in access to devices and data connectivity between high-income and low-income populations increasing the digital divide, social injustice, and inequality in the world (Bozkurt et al., 2020 ). In line with the SNA findings, the digital divide, manifesting itself most visibly in the inadequacy and insufficiency of digital devices and lack of high-speed Internet, can easily result in widespread inequalities. As such, the disparities between low and high socio-economic status families and school districts in terms of digital pedagogy inequality may deepen as teachers in affluent schools are more likely to offer a wide range of online learning activities and thereby secure better student engagement, participation, and interaction (Greenhow et al., 2020 ). These findings demonstrate that social inequities have been sharpened by the unfortunate disparities imposed by the COVID-19, thus requiring us to reimagine a future that mitigates such concerns.
• Theme 7: Future of education (See word path on Fig.  1 : Future  >  education  >  Covid19  >  pandemic  >  changes and pandemic  >  coronavirus, outbreak, impact  >  world ; See nodes on Fig.  2 : Sustainability, resilience, uncertainty, sdg4). Most significantly, COVID-19 the pandemic has shown the entire world that teachers and schools are invaluable resources and execute critical roles in society. Beyond that, with the compulsory changes resulting from the pandemic, it is evident that teaching and learning environments are not exclusive to brick-and-mortar classrooms. Digital technologies, being at the center of teaching and learning during the pandemic period, have been viewed as a pivotal agent in leveraging how learning takes place beyond the classroom walls (Quilter-Pinner & Ambrose, 2020 ). COVID-19 has made some concerns more visible. For example, the well-being of students, teachers, and society at large has gained more importance in these times of crisis. Furthermore, the need for educational technology and digital devices has compounded and amplified social inequities (Pelletier et al., 2021 ; West & Allen, 2020 ). Despite its global challenges, the need for technology and digital devices has highlighted some advantages that are likely to shape the future of education, particularly those related to the benefits of educational technology. For example, online learning could provide a more flexible, informal, self-paced learning environment for students (Adedoyin & Soykan, 2020 ). However, it also bears the risk of minimizing social interaction, as working in shared office environments has shifted to working alone in home-office settings. In this respect, the transformation of online education must involve a particular emphasis on sustaining interactivity through technology (Dwivedi et al., 2020 ). In view of the findings of the aforementioned studies, our text-mining and SNA findings suggest that the COVID-19 impositions may strongly shape the future of education and how learning takes place.

In summary, these themes extracted from the text-mining and SNA point to a significant milestone in the history of humanity, a multi-faceted reset that will affect many fields of life, from education and economics to sociology and lifestyle. The resulting themes have revealed that our natural response to an emerging worldwide situation shifted the educational landscape. The early response of the educational system was emergency-based and emphasized the continuance of in-person instruction via synchronous learning technologies. The subsequent response foregrounded the significance of digitally mediated learning pedagogy, related teacher competencies, and professional development. As various stakeholders (e.g., students, teachers, parents) have experienced a heightened level of anxiety and stress, an emerging strand of research has highlighted the need for care-based and trauma-informed pedagogies as a response to the side effects of the pandemic. In addition, as the global pandemic has made systemic impairments, such as social injustice and inequity, more visible, an important line of research has emerged on how social justice can be ensured given the challenges caused by the pandemic. Lastly, a sizable amount of research indicates that although the COVID-19 pandemic has imposed unprecedented challenges to our personal, educational, and social lives, it has also taught us how to respond to future crises in a timely, technologically-ready, pedagogically appropriate, and inclusive manner.

SNA: Citation Trends in the References of the Sampled Publications

The trends identified through SNA in citation patterns indicate two lines of thematic clusters (see Fig.  3 -A network graph depicting the citing and being cited patterns in the research corpus. Node sizes were defined by their citation count and betweenness centrality.). These clusters align with the results of the analysis of the titles, abstracts, and keywords of the sampled publications and forge the earlier themes (Theme 4: Emergency remote education and Theme 5: Pedagogy of care).

• Thematic Cluster 1: The first cluster centers on the abilities of educational response, emergency remote education affordances, and continuity of education (Bozkurt & Sharma, 2020a ; Crawford et al., 2020 ; Hodges et al., 2020 ) to mitigate the impact of COVID-19 on education, especially for more vulnerable and disadvantaged groups (UNESCO, 2020 ; Viner et al., 2020 ). The thematic cluster one agrees with the theme four emergency remote education . The first trend line (See red line in Fig.  3 ) shows that the education system is vulnerable to external threats. Considering that interruption of education is not exclusive to pandemics – for example, political crises have also caused disruptions (Rapp et al., 2016 ) – it is clear that coping mechanisms are needed to ensure the continuity of education under all conditions. In this case, we need to reimagine and recalibrate education to make it resilient, flexible, and adaptive, not only to ensure the continuity of education, but also to ensure social justice, equity, and equality. Given that online education has its own limitations (e.g., it is restricted to online tools and infrastructures), we need to identify alternative entry points for those who do not have digital devices or lack access to the internet.

“What we teach in these times can have secondary importance. We have to keep in mind that students will remember not the educational content delivered, but how they felt during these hard times. With an empathetic approach, the story will not center on how to successfully deliver educational content, but it will be on how learners narrate these times” (p. iv).

Conclusion and Suggestions

The results from this study indicate that quick adaptability and flexibility have been key to surviving the substantial challenges generated by COVID-19. However, extreme demands on flexibility have taken a toll on human well-being and have exacerbated systemic issues like inequity and inequality. Using data mining that involved network analysis and text mining as analytical tools, this research provides a panoramic picture of the COVID-19-related themes educational researchers have addressed in their work. A sample of 1150 references yielded seven themes, which served to provide a comprehensive meta-narrative about COVID-19 and its impact on education.

A portion of the sampled publications focused on what we refer to as the great reset , highlighting the challenges that the emergency lockdown brought to the world. A publication pattern centered around digital pedagogy posited distance and online learning as key components and identified the need for teacher training. Given the need for adaptability, a third theme revealed the demand for professional development in higher education and a future shift in educational roles. It can be recommended that future research investigate institutional policy changes and the adaptation to these changes in renewed educational roles. The ERE theme centered on the lack of preparation in instituting the forced changes brought about by the COVID-19 pandemic. The publications related to this theme revealed that the COVID-19 pandemic uncovered silent threads in educational environments, like depression, inequality, and injustice. A pedagogy of care has been developed with the aim of reducing anxiety and providing support through coping strategies. These research patterns indicate that the future of education demands sustainability and resilience in the face of uncertainty.

Results of the thematic analysis of citation patterns (Fig.  3 ) overlapped with two of the themes found in our thematic concept map (Fig.  1 ) and network graphic (Fig.  2 ). It was shown that researchers have emphasized the continuity of education and the psychological effects of the COVID-19 crisis on learners. Creating coping strategies to deal with global crises (e.g., pandemics, political upheavals, natural disasters) has been shown to be a priority for educational researchers. The pedagogy of resilience (Purdue University Innovative learning, n.d. ) provides governments, institutions, and instructors with an alternative tool to applying to their contexts in the face of hardship. Furthermore, prioritizing the psychological long-term effects of the crisis in learners could alleviate achievement gaps. We recommend that researchers support grieving learners through care (Noddings, 1984 ) and trauma-informed pedagogy (Imad, 2020 ). Our resilience and empathy will reflect our preparedness for impending crises. The thematic analysis of citation patterns (1: educational response, emergency remote education affordances, and continuity of education; 2: psychological impact of COVID-19) further indicates suggestions for future instructional/learning designers. Freire ( 1985 ) argues that to transform the world we need to humanize it. Supporting that argument, the need for human-centered pedagogical approaches (Robinson et al., 2020 ) by considering learning a multifaceted process (Hodges et al., 2021 ) for well-designed learning experiences (Moore et al., 2021 ) is a requirement and instructional/learning designers have an important responsibility not only to design courses but an entire learning ecosystem where diversity, sensitivity, and inclusivity are prioritized.

ERE is not a representative feature in the field of online education or distance education but rather, a forced reaction to extraordinary circumstances in education. The increasing confusion between the practice of ERE and online learning could have catastrophic consequences in learners' outcomes, teachers' instructional practices, and institutional policies. Researchers, educators, and policymakers must work cooperatively and be guided by sound work in the field of distance learning to design nourishing educational environments that serve students’ best interests.

In this study, text mining and social network analysis were demonstrated to be powerful tools for exploring and visualizing patterns in COVID-19-related educational research. However, a more in-depth examination is still needed to synthesize effective strategies that can be used to support us in future crises. Systematic reviews that use classical manual coding techniques may take more time but increase our understanding of a phenomenon and help us to develop specific action plans. Future systematic reviews can use the seven themes identified in this study to analyze primary studies and find strategies that counteract the survival of the fittest mindset to ensure that no student is left behind.

Acknowledgements

This paper is dedicated to all educators and instructional/learning designers who ensured the continuity of education during the tough times of the COVID-19 pandemic.

This article is produced as a part of the 2020 AECT Mentoring Program.

SNA of references covering pre-COVID-19 period (Only the first authors were labeled)

Authors’ Contributions

AB: Conceptualization, Methodology, Software, Formal analysis, Investigation, Resources, Data Curation, Writing—Original Draft, Writing—Review & Editing, Visualization, Funding acquisition.; KK: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; MT: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; ÖK: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.; DCR: Conceptualization, Investigation, Writing—Original Draft, Writing—Review & Editing.

This paper is supported by Anadolu University, Scientific Research Commission with grant no: 2106E084.

Data Availability

Declarations.

This is a systematic review study and exempt from ethical approval.

The authors declare no competing interests.

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Partisan divides over K-12 education in 8 charts

K-12 education is shaping up to be a key issue in the 2024 election cycle. Several prominent Republican leaders, including GOP presidential candidates, have sought to limit discussion of gender identity and race in schools , while the Biden administration has called for expanded protections for transgender students . The coronavirus pandemic also brought out partisan divides on many issues related to K-12 schools .

Today, the public is sharply divided along partisan lines on topics ranging from what should be taught in schools to how much influence parents should have over the curriculum. Here are eight charts that highlight partisan differences over K-12 education, based on recent surveys by Pew Research Center and external data.

Pew Research Center conducted this analysis to provide a snapshot of partisan divides in K-12 education in the run-up to the 2024 election. The analysis is based on data from various Center surveys and analyses conducted from 2021 to 2023, as well as survey data from Education Next, a research journal about education policy. Links to the methodology and questions for each survey or analysis can be found in the text of this analysis.

Most Democrats say K-12 schools are having a positive effect on the country , but a majority of Republicans say schools are having a negative effect, according to a Pew Research Center survey from October 2022. About seven-in-ten Democrats and Democratic-leaning independents (72%) said K-12 public schools were having a positive effect on the way things were going in the United States. About six-in-ten Republicans and GOP leaners (61%) said K-12 schools were having a negative effect.

About six-in-ten Democrats (62%) have a favorable opinion of the U.S. Department of Education , while a similar share of Republicans (65%) see it negatively, according to a March 2023 survey by the Center. Democrats and Republicans were more divided over the Department of Education than most of the other 15 federal departments and agencies the Center asked about.

In May 2023, after the survey was conducted, Republican lawmakers scrutinized the Department of Education’s priorities during a House Committee on Education and the Workforce hearing. The lawmakers pressed U.S. Secretary of Education Miguel Cardona on topics including transgender students’ participation in sports and how race-related concepts are taught in schools, while Democratic lawmakers focused on school shootings.

Partisan opinions of K-12 principals have become more divided. In a December 2021 Center survey, about three-quarters of Democrats (76%) expressed a great deal or fair amount of confidence in K-12 principals to act in the best interests of the public. A much smaller share of Republicans (52%) said the same. And nearly half of Republicans (47%) had not too much or no confidence at all in principals, compared with about a quarter of Democrats (24%).

This divide grew between April 2020 and December 2021. While confidence in K-12 principals declined significantly among people in both parties during that span, it fell by 27 percentage points among Republicans, compared with an 11-point decline among Democrats.

Democrats are much more likely than Republicans to say teachers’ unions are having a positive effect on schools. In a May 2022 survey by Education Next , 60% of Democrats said this, compared with 22% of Republicans. Meanwhile, 53% of Republicans and 17% of Democrats said that teachers’ unions were having a negative effect on schools. (In this survey, too, Democrats and Republicans include independents who lean toward each party.)

The 38-point difference between Democrats and Republicans on this question was the widest since Education Next first asked it in 2013. However, the gap has exceeded 30 points in four of the last five years for which data is available.

Republican and Democratic parents differ over how much influence they think governments, school boards and others should have on what K-12 schools teach. About half of Republican parents of K-12 students (52%) said in a fall 2022 Center survey that the federal government has too much influence on what their local public schools are teaching, compared with two-in-ten Democratic parents. Republican K-12 parents were also significantly more likely than their Democratic counterparts to say their state government (41% vs. 28%) and their local school board (30% vs. 17%) have too much influence.

On the other hand, more than four-in-ten Republican parents (44%) said parents themselves don’t have enough influence on what their local K-12 schools teach, compared with roughly a quarter of Democratic parents (23%). A larger share of Democratic parents – about a third (35%) – said teachers don’t have enough influence on what their local schools teach, compared with a quarter of Republican parents who held this view.

Republican and Democratic parents don’t agree on what their children should learn in school about certain topics. Take slavery, for example: While about nine-in-ten parents of K-12 students overall agreed in the fall 2022 survey that their children should learn about it in school, they differed by party over the specifics. About two-thirds of Republican K-12 parents said they would prefer that their children learn that slavery is part of American history but does not affect the position of Black people in American society today. On the other hand, 70% of Democratic parents said they would prefer for their children to learn that the legacy of slavery still affects the position of Black people in American society today.

Parents are also divided along partisan lines on the topics of gender identity, sex education and America’s position relative to other countries. Notably, 46% of Republican K-12 parents said their children should not learn about gender identity at all in school, compared with 28% of Democratic parents. Those shares were much larger than the shares of Republican and Democratic parents who said that their children should not learn about the other two topics in school.

Many Republican parents see a place for religion in public schools , whereas a majority of Democratic parents do not. About six-in-ten Republican parents of K-12 students (59%) said in the same survey that public school teachers should be allowed to lead students in Christian prayers, including 29% who said this should be the case even if prayers from other religions are not offered. In contrast, 63% of Democratic parents said that public school teachers should not be allowed to lead students in any type of prayers.

In June 2022, before the Center conducted the survey, the Supreme Court ruled in favor of a football coach at a public high school who had prayed with players at midfield after games. More recently, Texas lawmakers introduced several bills in the 2023 legislative session that would expand the role of religion in K-12 public schools in the state. Those proposals included a bill that would require the Ten Commandments to be displayed in every classroom, a bill that would allow schools to replace guidance counselors with chaplains, and a bill that would allow districts to mandate time during the school day for staff and students to pray and study religious materials.

Mentions of diversity, social-emotional learning and related topics in school mission statements are more common in Democratic areas than in Republican areas. K-12 mission statements from public schools in areas where the majority of residents voted Democratic in the 2020 general election are at least twice as likely as those in Republican-voting areas to include the words “diversity,” “equity” or “inclusion,” according to an April 2023 Pew Research Center analysis .

Also, about a third of mission statements in Democratic-voting areas (34%) use the word “social,” compared with a quarter of those in Republican-voting areas, and a similar gap exists for the word “emotional.” Like diversity, equity and inclusion, social-emotional learning is a contentious issue between Democrats and Republicans, even though most K-12 parents think it’s important for their children’s schools to teach these skills . Supporters argue that social-emotional learning helps address mental health needs and student well-being, but some critics consider it emotional manipulation and want it banned.

In contrast, there are broad similarities in school mission statements outside of these hot-button topics. Similar shares of mission statements in Democratic and Republican areas mention students’ future readiness, parent and community involvement, and providing a safe and healthy educational environment for students.

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About 1 in 4 U.S. teachers say their school went into a gun-related lockdown in the last school year

About half of americans say public k-12 education is going in the wrong direction, what public k-12 teachers want americans to know about teaching, what’s it like to be a teacher in america today, race and lgbtq issues in k-12 schools, most popular.

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Amanda Hoover

Students Are Likely Writing Millions of Papers With AI

Students have submitted more than 22 million papers that may have used generative AI in the past year, new data released by plagiarism detection company Turnitin shows.

A year ago, Turnitin rolled out an AI writing detection tool that was trained on its trove of papers written by students as well as other AI-generated texts. Since then, more than 200 million papers have been reviewed by the detector, predominantly written by high school and college students. Turnitin found that 11 percent may contain AI-written language in 20 percent of its content, with 3 percent of the total papers reviewed getting flagged for having 80 percent or more AI writing. (Turnitin is owned by Advance, which also owns Condé Nast, publisher of WIRED.) Turnitin says its detector has a false positive rate of less than 1 percent when analyzing full documents.

ChatGPT’s launch was met with knee-jerk fears that the English class essay would die . The chatbot can synthesize information and distill it near-instantly—but that doesn’t mean it always gets it right. Generative AI has been known to hallucinate , creating its own facts and citing academic references that don’t actually exist. Generative AI chatbots have also been caught spitting out biased text on gender and race . Despite those flaws, students have used chatbots for research, organizing ideas, and as a ghostwriter . Traces of chatbots have even been found in peer-reviewed, published academic writing .

Teachers understandably want to hold students accountable for using generative AI without permission or disclosure. But that requires a reliable way to prove AI was used in a given assignment. Instructors have tried at times to find their own solutions to detecting AI in writing, using messy, untested methods to enforce rules , and distressing students. Further complicating the issue, some teachers are even using generative AI in their grading processes.

Detecting the use of gen AI is tricky. It’s not as easy as flagging plagiarism, because generated text is still original text. Plus, there’s nuance to how students use gen AI; some may ask chatbots to write their papers for them in large chunks or in full, while others may use the tools as an aid or a brainstorm partner.

Students also aren't tempted by only ChatGPT and similar large language models. So-called word spinners are another type of AI software that rewrites text, and may make it less obvious to a teacher that work was plagiarized or generated by AI. Turnitin’s AI detector has also been updated to detect word spinners, says Annie Chechitelli, the company’s chief product officer. It can also flag work that was rewritten by services like spell checker Grammarly, which now has its own generative AI tool . As familiar software increasingly adds generative AI components, what students can and can’t use becomes more muddled.

Detection tools themselves have a risk of bias. English language learners may be more likely to set them off; a 2023 study found a 61.3 percent false positive rate when evaluating Test of English as a Foreign Language (TOEFL) exams with seven different AI detectors. The study did not examine Turnitin’s version. The company says it has trained its detector on writing from English language learners as well as native English speakers. A study published in October found that Turnitin was among the most accurate of 16 AI language detectors in a test that had the tool examine undergraduate papers and AI-generated papers.

Medea Giordano

Nena Farrell

Schools that use Turnitin had access to the AI detection software for a free pilot period, which ended at the start of this year. Chechitelli says a majority of the service’s clients have opted to purchase the AI detection. But the risks of false positives and bias against English learners have led some universities to ditch the tools for now. Montclair State University in New Jersey announced in November that it would pause use of Turnitin’s AI detector. Vanderbilt University and Northwestern University did the same last summer.

“This is hard. I understand why people want a tool,” says Emily Isaacs, executive director of the Office of Faculty Excellence at Montclair State. But Isaacs says the university is concerned about potentially biased results from AI detectors, as well as the fact that the tools can’t provide confirmation the way they can with plagiarism. Plus, Montclair State doesn’t want to put a blanket ban on AI, which will have some place in academia. With time and more trust in the tools, the policies could change. “It’s not a forever decision, it’s a now decision,” Isaacs says.

Chechitelli says the Turnitin tool shouldn’t be the only consideration in passing or failing a student. Instead, it’s a chance for teachers to start conversations with students that touch on all of the nuance in using generative AI. “People don’t really know where that line should be,” she says.

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AI Index Report

Welcome to the seventh edition of the AI Index report. The 2024 Index is our most comprehensive to date and arrives at an important moment when AI’s influence on society has never been more pronounced. This year, we have broadened our scope to more extensively cover essential trends such as technical advancements in AI, public perceptions of the technology, and the geopolitical dynamics surrounding its development. Featuring more original data than ever before, this edition introduces new estimates on AI training costs, detailed analyses of the responsible AI landscape, and an entirely new chapter dedicated to AI’s impact on science and medicine.

Read the 2024 AI Index Report

The AI Index report tracks, collates, distills, and visualizes data related to artificial intelligence (AI). Our mission is to provide unbiased, rigorously vetted, broadly sourced data in order for policymakers, researchers, executives, journalists, and the general public to develop a more thorough and nuanced understanding of the complex field of AI.

The AI Index is recognized globally as one of the most credible and authoritative sources for data and insights on artificial intelligence. Previous editions have been cited in major newspapers, including the The New York Times, Bloomberg, and The Guardian, have amassed hundreds of academic citations, and been referenced by high-level policymakers in the United States, the United Kingdom, and the European Union, among other places. This year’s edition surpasses all previous ones in size, scale, and scope, reflecting the growing significance that AI is coming to hold in all of our lives.

Steering Committee Co-Directors

Ray Perrault

Steering committee members.

Erik Brynjolfsson

John Etchemendy

Katrina Ligett

Terah Lyons

James Manyika

Juan Carlos Niebles

Vanessa Parli

Yoav Shoham

Russell Wald

Staff members.

Loredana Fattorini

Nestor Maslej

Letter from the co-directors.

A decade ago, the best AI systems in the world were unable to classify objects in images at a human level. AI struggled with language comprehension and could not solve math problems. Today, AI systems routinely exceed human performance on standard benchmarks.

Progress accelerated in 2023. New state-of-the-art systems like GPT-4, Gemini, and Claude 3 are impressively multimodal: They can generate fluent text in dozens of languages, process audio, and even explain memes. As AI has improved, it has increasingly forced its way into our lives. Companies are racing to build AI-based products, and AI is increasingly being used by the general public. But current AI technology still has significant problems. It cannot reliably deal with facts, perform complex reasoning, or explain its conclusions.

AI faces two interrelated futures. First, technology continues to improve and is increasingly used, having major consequences for productivity and employment. It can be put to both good and bad uses. In the second future, the adoption of AI is constrained by the limitations of the technology. Regardless of which future unfolds, governments are increasingly concerned. They are stepping in to encourage the upside, such as funding university R&D and incentivizing private investment. Governments are also aiming to manage the potential downsides, such as impacts on employment, privacy concerns, misinformation, and intellectual property rights.

As AI rapidly evolves, the AI Index aims to help the AI community, policymakers, business leaders, journalists, and the general public navigate this complex landscape. It provides ongoing, objective snapshots tracking several key areas: technical progress in AI capabilities, the community and investments driving AI development and deployment, public opinion on current and potential future impacts, and policy measures taken to stimulate AI innovation while managing its risks and challenges. By comprehensively monitoring the AI ecosystem, the Index serves as an important resource for understanding this transformative technological force.

On the technical front, this year’s AI Index reports that the number of new large language models released worldwide in 2023 doubled over the previous year. Two-thirds were open-source, but the highest-performing models came from industry players with closed systems. Gemini Ultra became the first LLM to reach human-level performance on the Massive Multitask Language Understanding (MMLU) benchmark; performance on the benchmark has improved by 15 percentage points since last year. Additionally, GPT-4 achieved an impressive 0.97 mean win rate score on the comprehensive Holistic Evaluation of Language Models (HELM) benchmark, which includes MMLU among other evaluations.

Although global private investment in AI decreased for the second consecutive year, investment in generative AI skyrocketed. More Fortune 500 earnings calls mentioned AI than ever before, and new studies show that AI tangibly boosts worker productivity. On the policymaking front, global mentions of AI in legislative proceedings have never been higher. U.S. regulators passed more AI-related regulations in 2023 than ever before. Still, many expressed concerns about AI’s ability to generate deepfakes and impact elections. The public became more aware of AI, and studies suggest that they responded with nervousness.

Ray Perrault Co-director, AI Index

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Write down your thoughts and shred them to relieve anger, researchers say

Writing negative reactions on paper and shredding it or scrunching and throwing in the bin eliminates angry feelings, study finds

Since time immemorial humans have tried to devise anger management techniques.

In ancient Rome, the Stoic philosopher Seneca believed “my anger is likely to do me more harm than your wrong” and offered avoidance tips in his AD45 work De Ira (On Anger).

More modern methods include a workout on the gym punchbag or exercise bike. But the humble paper shredder may be a more effective – and accessible – way to decompress, according to research.

A study in Japan has found that writing down your reaction to a negative incident on a piece of paper and then shredding it, or scrunching it into a ball and throwing it in the bin, gets rid of anger.

“We expected that our method would suppress anger to some extent,” said Nobuyuki Kawai, lead researcher of the study at Nagoya University. “However, we were amazed that anger was eliminated almost entirely.”

The study, published in Scientific Reports on Nature , builds on research on the association between the written word and anger reduction as well as studies showing how interactions with physical objects can control a person’s mood. For instance, those wanting revenge on an ex-partner may burn letters or destroy gifts.

Researchers believe the shredder results may be related to the phenomenon of “backward magical contagion”, which is the belief that actions taken on an object associated with a person can affect the individuals themselves. In this case, getting rid of the negative physical entity, the piece of paper, causes the original emotion to also disappear.

This is a reversal of “magical contagion” or “celebrity contagion” – the belief that the “essence” of an individual can be transferred through their physical possessions.

Fifty student participants were asked to write brief opinions about an important social problem, such as whether smoking in public should be outlawed. Evaluators then deliberately scored the papers low on intelligence, interest, friendliness, logic, and rationality. For good measure, evaluators added insulting comments such as: “I cannot believe an educated person would think like this. I hope this person learns something while at the university.”

The wound-up participants then wrote down their angry thoughts on the negative feedback on a piece of paper. One group was told to either roll up the paper and throw it in a bin or keep it in a file on their desk. A second group was told to shred the paper, or put it in a plastic box.

Anger levels of the individuals who discarded their paper in the bin or shredded it returned to their initial state, while those who retained a hard copy of the paper experienced only a small decrease in their overall anger.

Researchers concluded that “the meaning (interpretation) of disposal plays a critical role” in reducing anger.

“This technique could be applied in the moment by writing down the source of anger as if taking a memo and then throwing it away,” said Kawai.

Along with its practical benefits, this discovery may shed light on the origins of the Japanese cultural tradition known as hakidashisara ( hakidashi sara refers to a dish or plate) at the Hiyoshi shrine in Kiyosu, just outside Nagoya. Hakidashisara is an annual festival where people smash small discs representing things that make them angry. The study’s findings may explain the feeling of relief that participants report after leaving the festival, the paper concluded.

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