covid 19 impact on education research paper

The Impact of COVID-19 on Student Experiences and Expectations: Evidence from a Survey

In order to understand the impact of the COVID-19 pandemic on higher education, we surveyed approximately 1,500 students at one of the largest public institutions in the United States using an instrument designed to recover the causal impact of the pandemic on students' current and expected outcomes. Results show large negative effects across many dimensions. Due to COVID-19: 13% of students have delayed graduation, 40% lost a job, internship, or a job offer, and 29% expect to earn less at age 35. Moreover, these effects have been highly heterogeneous. One quarter of students increased their study time by more than 4 hours per week due to COVID-19, while another quarter decreased their study time by more than 5 hours per week. This heterogeneity often followed existing socioeconomic divides; lower-income students are 55% more likely to have delayed graduation due to COVID-19 than their higher-income peers. Finally, we show that the economic and health related shocks induced by COVID-19 vary systematically by socioeconomic factors and constitute key mediators in explaining the large (and heterogeneous) effects of the pandemic.

Noah Deitrick and Adam Streff provided excellent research assistance. All errors that remain are ours. 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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Research Article

Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process

Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Software, Visualization, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliations Instituto de Física de Líquidos y Sistemas Biológicos (UNLP-CONICET), La Plata, Argentina, Departamento de Ciencias Básicas, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), La Plata, Argentina, Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC-UIB), Campus UIB, Palma de Mallorca, Spain

Roles Data curation, Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing

Affiliation Departamento de Física Médica, Centro Atómico Bariloche, CONICET, CNEA, Bariloche, Argentina

Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review & editing

Affiliation Departamento de Estadística, Centro Regional Universitario Bariloche (CRUB) Universidad Nacional del Comahue (UNCOMA), Neuquén, Argentina

Roles Conceptualization, Formal analysis, Investigation, Methodology, Supervision, Writing – original draft, Writing – review & editing

Affiliations División Física Estadística e Interdisciplinaria, Centro Atómico Bariloche and CONICET, Bariloche, Argentina, Profesorado en Física, Universidad Nacional de Río Negro (UNRN), Bariloche, Argentina

• Fátima Velásquez-Rojas, 
• Jesus E. Fajardo, 
• Daniela Zacharías, 
• María Fabiana Laguna

• Published: September 7, 2022
• https://doi.org/10.1371/journal.pone.0274039
• Reader Comments

The COVID-19 pandemic abruptly changed the classroom context and presented enormous challenges for all actors in the educational process, who had to overcome multiple difficulties and incorporate new strategies and tools to construct new knowledge. In this work we analyze how student performance was affected, for a particular case of higher education in La Plata, Argentina. We developed an analytical model for the knowledge acquisition process, based on a series of surveys and information on academic performance in both contexts: face-to-face (before the onset of the pandemic) and virtual (during confinement) with 173 students during 2019 and 2020. The information collected allowed us to construct an adequate representation of the process that takes into account the main contributions common to all individuals. We analyzed the significance of the model by means of Artificial Neural Networks and a Multiple Linear Regression Method. We found that the virtual context produced a decrease in motivation to learn. Moreover, the emerging network of contacts built from the interaction between peers reveals different structures in both contexts. In all cases, interaction with teachers turned out to be of the utmost importance in the process of acquiring knowledge. Our results indicate that this process was also strongly influenced by the availability of resources of each student. This reflects the reality of a developing country, which experienced prolonged isolation, giving way to a particular learning context in which we were able to identify key factors that could guide the design of strategies in similar scenarios.

Citation: Velásquez-Rojas F, Fajardo JE, Zacharías D, Laguna MF (2022) Effects of the COVID-19 pandemic in higher education: A data driven analysis for the knowledge acquisition process. PLoS ONE 17(9): e0274039. https://doi.org/10.1371/journal.pone.0274039

Editor: Jianguo Wang, China University of Mining and Technology, CHINA

Received: September 7, 2021; Accepted: August 19, 2022; Published: September 7, 2022

Copyright: © 2022 Velásquez-Rojas et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

The process of acquiring knowledge is one of the most complex for the human being since it involves individual and social processes that have been studied by various epistemological currents [ 1 ]. The educational context where this process is developed is of great relevance since it represents the meeting space between teachers and students, in which a fundamental part of the construction of new knowledge occurs.

The COVID-19 pandemic abruptly changed this context with classroom closures of unprecedented extent and duration, disrupting conventional education in schools and universities around the world. Such measures were an extension of the isolation established in many countries to mitigate the effects of COVID-19, given that social distancing proved to be one of the most effective strategies [ 2 – 10 ].

The educational community as a whole made an enormous effort to quickly adapt to the distance and online learning that this lockdown brought [ 11 ], but it is no less true that students were forced to rely much more on their own resources to sustain the continuity of their learning during this period [ 11 – 13 ]. In the particular case of Argentina the confinement measures began on March 20, 2020, affected all educational levels and coincided with the beginning of the first semester of the academic year.

The new educational context not only brought about great challenges but was also reflected in the results obtained by the students [ 14 – 18 ]. The effects of the change in the learning conditions, although recent and still in process, have been analyzed from different perspectives [ 11 – 17 ]. A less explored methodology, which we propose to address here, is to study this problem from the point of view of complex systems, in line with what was done by some of the authors of this work just before the start of the pandemic [ 19 ]. The reason behind choosing this research design lies in the fact that the approach from this perspective allows the interactions between the individuals involved to be adequately considered when analyzing the effect of a global variable, such as the pandemic. But in addition, the usefulness of mathematical modeling to unravel the relevance of different factors that are present in the knowledge acquisition process was demonstrated in our previous work.

In [ 19 ] we developed an analytical model (the KA model) based on data from a series of surveys that are contrasted with information on academic performance of students, to analyze how the knowledge acquisition depends globally on different extrinsic and intrinsic factors. Regarding the intrinsic factors, one that contributes greatly to the acquisition of knowledge of students is motivation, and this is precisely one of the most affected by the pandemic [ 20 ]. According to the EU report [ 14 ], the closure of physical schools and the adoption of distance education can negatively affect student learning through four main channels: less time spent learning, symptoms of stress, a change in the way that students interact, and lack of motivation to learn. But, it is possible to use a model to assess the hypothesis that lack of motivation is one of the strongest negative impacts of the pandemic on students, regardless of their personal characteristics? In particular, and since the KA model was developed for a specific (face-to-face) context, the first question to be answered in this work should be whether this model is sensitive to modifications of the educational context.

On the other hand, it was already mentioned that the change in physical context affected extrinsic factors that contribute to the acquisition of knowledge, such as the interaction with peers and teachers. This interaction has been found to be essential for the development of positive self-esteem, self-confidence, and a sense of identity. In fact, there is significant evidence showing that social skills are positively associated with cognitive skills and school achievement [ 21 , 22 ]. In this regard, a series of questions arise: From the perspective of the students, did the bond with teachers improve or worsen during the pandemic? Did the interaction between peers change with the change of context? What aspects of it can be measured in the new context?

Analyzing the consequences of the pandemic on the educational performance is a matter of global importance. It is well known that the distance education is essential to ensure the continuity of learning in situations in which face-to-face classes are suspended. In places where virtual and remote strategies were already becoming a reality, the change was a positive [ 18 ]. However, in other countries something as basic as Internet access is still a privilege, guaranteeing distance education cannot be taken for granted. The preparation (or lack thereof) of some countries in this area has revealed the weaknesses of educational methodologies and resources [ 13 ]. Bringing this situation to light is one more step towards fairness.

The previous statements prompt us to seek answers about how much the academic performance of students was affected by the change in the educational context caused by the pandemic. In addition, and in relation to the KA model, we would like to evaluate whether the aspects that we consider relevant have a comparable importance in the construction of knowledge, as well as the consistency of these results when comparing both scenarios.

In this new approach we adapt the analytical model presented in [ 19 ] to compare the knowledge acquisition process in two different contexts: face-to-face (before the onset of the pandemic) and virtual (during the confinement), for a particular case in higher education in Argentina. We present a study that involves 173 students and its entire evolution during 2019 and 2020 in both contexts. Furthermore, and in order to assess the relevance of the parameters we chose for our model, we apply two robust and versatile tools used in multiple applications: Artificial Neural Networks and a Multiple Linear Regression Method.

The article is organized as follows: in the Methods section we describe the participants and its educational context, the data collection and variables (which include the surveys used to construct our data-based model) and the different approaches used to fit the parameters of the model. Then, we present the main results of this work and finally, we summarize and discuss our findings.

Educational context

The research was carried out with several sections of students who attended the Physics II course, corresponding to the second year of Engineering careers at the Faculty of Engineering of the National University of La Plata (UNLP) [ 23 ], Argentina, during the years 2019 and 2020. The Faculty offers 13 engineering degrees, so the interest of the students in the course can vary greatly.

The complete course lasts one semester, with a workload of 8 hours per week divided into 2 theoretical-practical classes. The course consists of two parts, at the end of which a partial written test is taken with a score between 0 and 10. There are two approval regimes: direct promotion, which implies being exempt from the final test (if the average between the two partial exams is 6 or more) or promotion by final exam (if the average is between 4 and 6). Partial tests have an instance of recuperation during the semester and another at the end of it, where the student can improve any of the lower scores obtained in previous tests. This organization was also maintained during the confinement (in virtual context).

Participants

The first part of the research was done during the two semesters of the year 2019, with four different sections in face-to-face context for a total of 81 students (50 male, 31 female). The second part was developed during the year 2020 and also involved four different sections in two semesters, for a total of 92 students (61 male, 31 female). In all cases we had access to the final grade they obtained in the course. In both contexts, we worked with 4 different sections of students for a total of 8 sections, 173 students in 2 years. The initial group of students was much larger, however there were 173 who participated in the whole process. These data are reported in the ( S1 File ) and has been collected with the following actions:

• It does not involve minors.
• It has been collected anonymously. Students have been identified by a numerical code, avoiding gathering of any personal information.
• Students have been informed by the lecturers that some information about their activity could be anonymously collected for statistical purposes. Authors of this study did not receive any objections.
• The tasks related to this study were completely voluntary and they did not in any form alter students’ activities, classes, or the assessment process.

Considering these circumstances, we do not need to apply for ethics approval from our university since no personal data, minors or potentially hazardous activities were involved in the study.

Besides, all teachers involved in the study (some of them also co-authors of this manuscript) who were responsible for the subject taught also gave consent to carry out the study.

We obtained verbal consent from all the participants in the study.

Data collection and variables

We are interested in analyzing and comparing the processes observed in both contexts in terms of the KA model presented in [ 19 ]. A first step consisted in carrying out a classification such as that proposed by Bordogna and Albano [ 24 ] and which proved to be useful in our previous work. This involved separating the students into three different groups according to their final achievements K f , which we relate to the final grade obtained in the course. This was done as follows: (a) High-achieving (HA) students: 8 ≤ K f ≤ 10, (b) Average-achieving (AA) students: 6 < K f < 8 and (c) Low-achieving (LA) students: K f ≤ 6. It is worth noting that students with a final grade lower than 4 are not included in this study.

In Table 1 we show the number of students who participated in the work divided according to their final achievements K f , that we relate to the final grade obtained in the course. Interestingly, and as we found in [ 19 ], the groups have qualitatively different characteristics regarding the relevance of the factors considered in the construction of the new knowledge, as it will be clear shortly.

• PPT PowerPoint slide
• PNG larger image
• TIFF original image

https://doi.org/10.1371/journal.pone.0274039.t001

In Fig 1 we display the final grades obtained for all students that we include in the present work. In filled symbols we plot the data in the face-to-face context and the empty symbols represent the data in the virtual context. These data provide us with the information to contrast our theoretical model. A first look at this graph reveals that the marks obtained in the two contexts were different for the HA and LA groups, while the AA group did not present differences. HA students, whose grades were higher than 8, had on average a better performance in virtual context than in face-to-face context. The opposite is seen with the Low-achievement students, LA. To analyze the possible causes of these differences is one of the main purposes of the present paper.

https://doi.org/10.1371/journal.pone.0274039.g001

KA model for both contexts.

It is worth noting that in our study the contribution of peers to the acquisition of knowledge was gathered in two ways: the group conformation and the peer interaction itself. The group conformation includes information on the spatial distribution of the students and the formation of groups, obtained through direct observations of the classes before confinement and through questions in online surveys during confinement. An analysis of the differences in the structure of the peer network formed in each context is carried out in Fig 4 in the Results section.

During the virtual context, important and complementary information was also collected, such as resources the students had (work-space, technological equipment) and the context itself and how it was perceived. Although they are not included as terms in Eq 1 , we carry out a description of the observed situation in the S2 File .

Finally, it should be noted that in our study we focus on a specific type of learning, related to scientific concepts of classical physics. While we are aware that this is not the only value learned in the classroom, we simplify the concept of knowledge to use the final grade as a concrete and quantifiable measure of the student’s performance.

Here we present the surveys carried out on students during each semester of classes ( Table 2 ). The numbers and letters in the last column correspond to the values that we assign to each of them, in order to transfer the answers to the KA model of Eq 1 . The questions marked with (*) were reformulated to adapt them to the virtual context. The surveys carried out in the virtual context were delivered and completed in a digital way using Google tools, while those corresponding to the pre-confinement stage were delivered personally and were completed manually.

Although the surveys were broader, here we only include the questions involved in the model.

https://doi.org/10.1371/journal.pone.0274039.t002

The quantities evaluated more than once (as is the case of M or T ) were averaged in order to have a single value for each factor. Besides, the combination of strategies for the question that measures the interaction with the teacher T in the third survey was given the following numerical values: ABC = AB = AC = BC = 1, A = B = 0.7, C = AD = BD = ABD = ACD = BCD = 0.5, CD = 0.3, D = 0.1 (students could mark several options). These values were given to enhance the use of the strategies provided by the specific section to which the students belonged (options A, B).

From surveys to KA model.

https://doi.org/10.1371/journal.pone.0274039.t003

Proposed tools for analysis

As it was already mentioned, each of these groups has different characteristics regarding the relevance of the factors considered in the construction of Eq 1 . To explicitly measure the weight of each of them we apply two different and complementary approaches: Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

In reference [ 25 ], the capability of the ANN for estimating parameters of complex nonlinear and linear problems has been shown. A single-layer perceptron (SLP) constitutes a particular case of the ANN whose output equation resembles Eq 1 . This allows to cross-validate the MLR, which is the most common form of linear regression analysis to treat this kind of problem.

Single Layer Perceptron (SLP) network overview.

To reproduce Eq 1 from an ANN architecture we employed a SLP [ 25 ]. This type of ANN constitutes a particular case of a Multilayer Perceptron (MLP) [ 26 ]. The SLP is a feedforward network of a single artificial neuron-like unit, whose x j inputs (disposed akin to biologic dendrites) are multiplied by a corresponding weight w j and this product is passed to a neuron-like unit where the aforementioned product is added up, as shown in Fig 2 .

The usual ANN notation is in black text and in red text, the equivalent terms corresponding to this particular work are shown. (See Eq 1 ). The element-wise product between the inputs and the weights are added up in the “net input function” stage and suppressing the activation function, an output corresponding to the linear combination of the inputs and the weights is obtained.

https://doi.org/10.1371/journal.pone.0274039.g002

The SLP model was implemented in the programming language Python by means of the Keras package [ 27 ].

Multiple Linear Regression Method.

Besides, β M , β T , β P , β HA , β LA and β F are the regression coefficients corresponding to the variables M , T , P , HA , LA and F , respectively, and they were estimated through the OLS (Ordinary Least Squares) method.

This model was fitted using the function lm() in the programming language R version 4.1.0 [ 28 ].

Comparison between contexts

In our previous work [ 19 ], we compared the results of our KA model with the final grade that the students obtained. Looking for an answer to our main question, about how the educational context affected student performance, we first compare the general results in both, face-to-face and virtual contexts.

We proposed in Eq 1 that the final knowledge reached by a student on a given topic is mainly due to three contributing factors, the personal motivation ( M ), the influence of the teachers ( T ) and the influence of peers ( P ). In Fig 3 we show the average values of the final grade of each group, < K f >, together with average of the data obtained from the surveys carried out, in order to analyze and compare the differences observed with the change of context.

(a) Final grade < K f >, (b) motivation M , (c) interaction with teachers T and (d) interaction between peers P .

https://doi.org/10.1371/journal.pone.0274039.g003

These results allow us to respond positively to our first question, about whether our approach is sensitive to changes in the educational context. As we can see, although the KA model was originally developed for a specific context (face to face), the values of the variables are different for both contexts.

In Fig 3(a) we show the average final grade < K f > for each group of students (HA, AA and LA) and in both contexts. We observe again the differences we first noticed in Fig 1 , related to how the performance of each group is modified with the change of context. For HA students, < K f > increased during the virtual context while for AA and LA it decreased. In what follows, and to deepen the understanding of what is observed, we will analyze what was obtained for the three contributing factors (also averaged for each group), and that we plot in panels (b), (c) and (d) of Fig 3 .

The values of motivation presented in Fig 3(b) reflects a widely studied aspect of the psychological impact of the pandemic on students [ 14 , 20 ]. Our results clearly report the impact of the virtual context on the motivation of students, no matter the group they belong to. This fact should in itself be an alarm to build policies to support the mental health and educational success of the students at all times. If motivation dropped notably in the new virtual context, and the final knowledge is considered as the sum of several factors that contribute to the acquisition of this knowledge, then the way of interacting with peers and teachers also had to change.

The general decrease in the virtual context observed in motivation is not repeated in the other factors analyzed in this study. Fig 3(c) gives us information about the teacher’s contribution from the students’ perspective. Note that for the HA group it has the same weight in both contexts (face-to-face and virtual), while for the AA and LA groups the interaction with teachers increased in the virtual context. Generally, the teacher acts as an intermediary between the activities carried out by the students in order to assimilate the new knowledge and in this new context their presence and support (albeit virtual) was fundamental for many students.

Finally, in Fig 3(d) , we can see the differences in the interaction between peers for each group of students, another issue that was affected during the pandemic.

We can see that HA’s enriched the study in groups in the virtual context in contrast to the other groups of students. We also found that the structure of the emerging contact network from peer interaction presents very different characteristics in both contexts. More details about this aspect of the problem are presented in the next subsection. The situation observed in Fig 3(d) for the interaction between peers is the one that most reflects the behavior of the general performance ( Fig 3(a) ), however the trend is attenuated due to what is observed in Fig 3(b) and 3(c) . These results may partially respond to the change observed in the way students interact.

The aforementioned results can be summarized in Table 4 where we show the relative changes between both contexts. This quantity expresses what it was observed in Fig 3 with the raw data obtained in the surveys: A strong decrease in the motivation term for all groups of students, and different trends in the way of interacting with peers and with teachers depending on the group to which the students belong.

https://doi.org/10.1371/journal.pone.0274039.t004

Networks of peer interactions.

The analysis carried out around Fig 3 indicates that the change in physical context modified the way in which students interact with each other. Furthermore, in this area data was collected in different ways depending on the context. In the face-to-face context, the observations in the classroom were made in situ, with photographic records and paper surveys. During the virtual context, the surveys were digital using Google tools as mentioned above. In the latter case, no observations could be made, so the students were asked how their interaction with the group was and with whom they specifically interacted. This fact could result in a lack of information for this context. However, that was not the case, since although the information collected in both cases is not completely comparable, they suggest a change in behavior in the relationship between peers. Table 5 expresses the number of students who were observed grouped or isolated during the face-to-face classes. Likewise, for the virtual case, the number of students who affirmed to study or not in a group is reported. We find that the percentage of isolated students decreased from 37% to 26% with the change of context. Interestingly, the increase in interaction between students in the virtual context was observed to a greater or lesser extent for the three groups.

https://doi.org/10.1371/journal.pone.0274039.t005

To deepen the understanding of how students modified their way of interacting, we draw in Fig 4(a) the network that represents the students before confinement (face-to-face context) for N = 81. As we said, the data was obtained from direct observations in the classroom, where the nodes represent the students (divided in the HA, AA and LA groups) and the links their interactions. Note that here we use double bonds, indicating a reciprocal interaction.

(a) Network scheme from classroom observations in face-to-face context where the links are reciprocal interactions. (b) Network scheme from data obtained through surveys in virtual context. The links can be or not be reciprocal interactions. The nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

https://doi.org/10.1371/journal.pone.0274039.g004

Besides, in Fig 4(b) we show the network that describes the students in virtual context for N = 92. The data come from the surveys carried out, and again the nodes represent the students divided in the groups HA, AA and LA. We use links to represent their interactions, although now they are double or single, as the responses to the surveys given by the students may or may not be reciprocal. Moreover, the nodes marked with an asterisk represent students who claimed to interact with students from another section who did not participate in this study.

A comparison between both networks indicates some similarities, such as the presence of highly connected clusters, as well as isolated students. However, the network corresponding to the virtual context has nodes that connect two different clusters, acting as “bridges”. This was not observed in the face-to-face context and could mean a new form of relationship between students. This result deepens the understanding of the effect that the pandemic has on peer relationships, and answers some of the questions asked in the introduction on this topic.

Measure of the relevance of the terms that influence the knowledge acquisition process

A way to validate the model presented in Eq 1 is to analyze the relevance of the terms that compose it. In our previous work [ 19 ] we did it by adding coefficients to each factor of the KA model. These coefficients could be interpreted as the relative weight that each term in Eq 1 has, and were chosen so that the average value calculated with the model for each group is as close as possible to the average value of the actual final grades obtained. In order to analyze the relevance and consistency of the factors that we chose to describe the knowledge acquisition process, we now we choose two different and complementary approaches to find the weight of each term of Eq 1 : Artificial Neural Networks (ANN) and a Multiple Linear Regression Method (MLR).

ANN approach.

https://doi.org/10.1371/journal.pone.0274039.g005

https://doi.org/10.1371/journal.pone.0274039.t006

Finally, in Fig 6 we present a comparison between the final grade for each student and the final knowledge obtained from Eq 1 (KA model) with the coefficients obtained with the ANN approach. The global behavior of the KA model follows the general trend of the data. The observed dispersion is due to the presence of particular cases, whose complete evolution is not captured by the model. In our previous work [ 19 ] we made an analysis of some particular cases like these.

https://doi.org/10.1371/journal.pone.0274039.g006

MLR approach.

We make use of the Multiple Linear Regression Method in order to find the weights of each contributing factor of the KA model, and compare them with the ones obtained in the previous section. The results are shown in Table 7 , where we express the values for β , SE (standard error) and p-value for the terms of the Eq 3 .

https://doi.org/10.1371/journal.pone.0274039.t007

The p-values obtained show that all beta regression coefficients are statistically significant. Assumptions of linearity, independence, homoscedasticity and normality were checked, as well as the presence of influential values.

At last, we show in Fig 7 a comparison between the final grade for each student and the final knowledge of Eq 1 (KA model) with the coefficients obtained with the MLR approach. Again, the K f obtained with the model behaves similarly to the data. It should be noted the similarity of the result obtained in Figs 5 and 6 with that shown in Fig 2 of [ 19 ]. In the present work, the adjustment of the weights that gave rise to both figures was carried out in a more appropriate way than in that paper, where the coefficients of each term were chosen exploratory.

https://doi.org/10.1371/journal.pone.0274039.g007

In a previous work we proposed to describe the knowledge acquisition process as a dynamic quantity composed of several terms, where it was implicit that such a process was carried out in the classroom. But, what happens when the physical place where this complex socio-cultural construction takes place changes? What are the consequences of that specific educational context being taken from one day to the next? We seek to answer these questions by discussing how the terms of the knowledge acquisition model were modified, and which ones most directly influenced student performance during the transition to virtuality.

For that, we analyze the knowledge acquisition process in face-to-face and virtual contexts for a specific study case. Our investigation spanned two years and involved 173 students, observing the evolution of their learning process for each particular context.

Inspired by the work of Ref. [ 19 ], we wanted to assess whether the observed changes in academic performance can be understood from a model that incorporates the main factors that contribute to the knowledge acquisition process. The KA model is an analytical model based on data, which incorporates information from a series of surveys and whose results are contrasted with information on academic performance. The surveys were carried out 3 times during each semester and reflected the feelings of the students during their learning process that influenced their performance.

The raw data in Fig 1 show that the final grade of the students in both contexts presented differences. Specifically, the grades of the students with High-achievement (HA) were better in virtual context than in face-to-face context. The opposite is seen with Low-achieving students (LA), while the intermediate performance group (AA) did not show differences. In the results shown in [ 18 ], the performance of the students who used remote learning tools showed an improvement in the virtual context. We believe that this difference is due to the fact that having better resources positioned them in a privileged place with respect to the case studied in this work.

The results obtained in Fig 3 reinforce accepted ideas related to the importance of motivation in the learning process: the switch to virtual context caused a negative impact on the motivation of the entire student population, but was strongly reflected in the performance of LA students. This fact should alert the educational community and especially those responsible for building support mechanisms for the mental health of students. Furthermore, we observe that the new context generates a change in the way students interact with their peers and teachers. In particular, the HA students did not modify the interaction with the teachers (maintaining high values in both contexts) while they strengthened the study in groups in the virtual context, unlike the rest of the groups. For AA and LA students, interaction with teachers increased in the virtual context, and this result highlights the importance of the teacher’s role as a consultant and as fundamental support for students.

We also find that the structure of the network of contacts that is formed between peers in both contexts presents some common characteristics, as well as some interesting differences, as we saw in Fig 4 . Among the first is that both networks have highly connected clusters, as well as a significant number of completely isolated students. The virtual context network, however, shows a feature not observed in the other network: the presence of individuals who interact with one or more students from different clusters. These individuals act as bridges between students who otherwise would not be connected. These structures could be reflecting a new form of relationship between students that occurs more easily in the virtual context. Nevertheless, we are aware that this analysis requires a more detailed investigation that is beyond the scope of this work with the data we currently have. On the other hand, it is also true that the virtual context made it possible to record that the interactions between the students go beyond what happens in the classroom space.

Related with the previous analysis is the fact that, although the equation in the KA model is linear, the term of peers can be interpreted as an effective version of a real non-linear interaction. This term in itself adds complexity to the model since group interaction does not obey “linear” rules. However, the simplification made in the KA model remains valid in light of the results obtained in [ 19 ] and are in line with the idea that the learning process is not limited to the interactive behavior of individual teachers and students, but should be understood in terms of collaborative behavior [ 29 ].

In order to find out the relevance of the factors that we included in the KA model, we used two different approaches: a standard Multiple Linear Regression Method and a Single Layer Perceptron, which is a particular type of Artificial Neural Network.

The results obtained with the neural network ( Fig 5 ) indicate that in both contexts the weights are similar. This result also shows that the raw results adequately describe each context, since the data obtained in each situation reflect the particular reality that each group of students is going through.

Moreover, both approaches indicate a greater relevance of the term of interaction with teachers. We were able to collect information from the teachers to support this fact and the perception of the change in the interaction with the students was also commented on by them (see S2 File ). The knowledge acquisition process comes hand in hand with the importance of the interaction with teachers, and the literalness of their presence in the accompaniment during learning. This result also confirms in some way the universality of the educational act.

The comparisons of Figs 6 and 7 between the raw data and the results obtained with the KA model indicate that the general behavior of individuals can be suitably described with Eq 1 , which is simply the sum of the relative contributions of each of the proposed factors: personal motivation, interaction with peers and influence of teachers. The robustness of the coefficients obtained with the two approaches also indicates that the information collected in the surveys and observations was sufficient to construct an adequate representation of the process. We are aware that this simplification leaves out a huge number of variables that are integrated to give rise to the unique process that each person experiences. But we believe that the results obtained allow us to validate our choice of factors as the main contributions common to all individuals.

Now, we discuss some considerations on the scope and limitations of this work.

One is that we must not lose sight of the fact that the change in the specific physical context brought with it a change in the evaluation criteria. Actually, this aspect was addressed in the teacher interviews that we summarize in the ( S2 File ). As K f is a hard data (the final grade obtained in the course), it would be more appropriate to build new models that consider these data in a more comprehensive way, taking into account the challenges that arose due to the change in this educational context.

Another important issue that is absent from the KA model is the personal context of the students and their available resources. The reason why it was not included is because we had no survey done on these topics in the face-to-face period, so it was not possible to compare both contexts. However, in the Supplementary Material ( S2 File ) we include additional information regarding this subject obtained from the surveys carried out in the virtual context. When asking the students for their feelings regarding confinement, the responses were varied but reluctance was reflected in more than half of the responses. This coincides with our observation about the lack of motivation (see Fig 3(b) ). The emotional stress, widely discussed in this context, goes beyond the academic environment and it was an important characteristic that we tried to capture with our research. Moreover, we found some relevant differences between the students of the different groups, which could influence their performance. Among them, a third of the students belonging to the LA group said they had a poor Internet connection in contrast to the HA group in which this situation occurred for a sixth of the students. More importantly, 13% of students belonging to the LA group did not have a laptop computer and 30% did not have an adequate study space.

These results show how the pandemic has increased educational inequalities at the economic, technological, social and even emotional level of the actors in the educational process. The virtual context promoted a change in teaching and learning methodologies, but it also brought another great challenge that is still far from being resolved, namely access to resources for all students. Hence the importance of recognizing inequalities to make visible the urgent need to build university policies that improve this situation.

A final though has to do with the generalizability of our results. Although this study was done for a specific case, the main factors analyzed here (motivation, interaction with peers and teachers) are not isolated from the global scenario. The generalization of the KA model to other educational scenarios is not only possible but quite straightforward. It should be noted, however, that the part of our study referring to the virtual context was carried out during the first year of the pandemic, so the results obtained could be strongly influenced by the transition between both contexts. Nevertheless, we believe they are valuable in themselves and can serve to deepen the understanding of the complex process of learning.

Supporting information

S1 file. survey data: numerical values associated with the ka model..

https://doi.org/10.1371/journal.pone.0274039.s001

S2 File. Additional information obtained from student and teacher surveys.

https://doi.org/10.1371/journal.pone.0274039.s002

Acknowledgments

The authors acknowledge Dr. José Javier Ramasco for his helpful suggestions on data analysis and availability.

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The pandemic has had devastating impacts on learning. What will it take to help students catch up?

Subscribe to the brown center on education policy newsletter, megan kuhfeld , megan kuhfeld senior research scientist - nwea @megankuhfeld jim soland , jim soland assistant professor, school of education and human development - university of virginia, affiliated research fellow - nwea @jsoland karyn lewis , and karyn lewis director, center for school and student progress - nwea @karynlew emily morton emily morton research scientist - nwea @emily_r_morton.

March 3, 2022

As we reach the two-year mark of the initial wave of pandemic-induced school shutdowns, academic normalcy remains out of reach for many students, educators, and parents. In addition to surging COVID-19 cases at the end of 2021, schools have faced severe staff shortages , high rates of absenteeism and quarantines , and rolling school closures . Furthermore, students and educators continue to struggle with mental health challenges , higher rates of violence and misbehavior , and concerns about lost instructional time .

As we outline in our new research study released in January, the cumulative impact of the COVID-19 pandemic on students’ academic achievement has been large. We tracked changes in math and reading test scores across the first two years of the pandemic using data from 5.4 million U.S. students in grades 3-8. We focused on test scores from immediately before the pandemic (fall 2019), following the initial onset (fall 2020), and more than one year into pandemic disruptions (fall 2021).

Average fall 2021 math test scores in grades 3-8 were 0.20-0.27 standard deviations (SDs) lower relative to same-grade peers in fall 2019, while reading test scores were 0.09-0.18 SDs lower. This is a sizable drop. For context, the math drops are significantly larger than estimated impacts from other large-scale school disruptions, such as after Hurricane Katrina—math scores dropped 0.17 SDs in one year for New Orleans evacuees .

Even more concerning, test-score gaps between students in low-poverty and high-poverty elementary schools grew by approximately 20% in math (corresponding to 0.20 SDs) and 15% in reading (0.13 SDs), primarily during the 2020-21 school year. Further, achievement tended to drop more between fall 2020 and 2021 than between fall 2019 and 2020 (both overall and differentially by school poverty), indicating that disruptions to learning have continued to negatively impact students well past the initial hits following the spring 2020 school closures.

These numbers are alarming and potentially demoralizing, especially given the heroic efforts of students to learn and educators to teach in incredibly trying times. From our perspective, these test-score drops in no way indicate that these students represent a “ lost generation ” or that we should give up hope. Most of us have never lived through a pandemic, and there is so much we don’t know about students’ capacity for resiliency in these circumstances and what a timeline for recovery will look like. Nor are we suggesting that teachers are somehow at fault given the achievement drops that occurred between 2020 and 2021; rather, educators had difficult jobs before the pandemic, and now are contending with huge new challenges, many outside their control.

Clearly, however, there’s work to do. School districts and states are currently making important decisions about which interventions and strategies to implement to mitigate the learning declines during the last two years. Elementary and Secondary School Emergency Relief (ESSER) investments from the American Rescue Plan provided nearly $200 billion to public schools to spend on COVID-19-related needs. Of that sum, $22 billion is dedicated specifically to addressing learning loss using “evidence-based interventions” focused on the “ disproportionate impact of COVID-19 on underrepresented student subgroups. ” Reviews of district and state spending plans (see Future Ed , EduRecoveryHub , and RAND’s American School District Panel for more details) indicate that districts are spending their ESSER dollars designated for academic recovery on a wide variety of strategies, with summer learning, tutoring, after-school programs, and extended school-day and school-year initiatives rising to the top.

Comparing the negative impacts from learning disruptions to the positive impacts from interventions

To help contextualize the magnitude of the impacts of COVID-19, we situate test-score drops during the pandemic relative to the test-score gains associated with common interventions being employed by districts as part of pandemic recovery efforts. If we assume that such interventions will continue to be as successful in a COVID-19 school environment, can we expect that these strategies will be effective enough to help students catch up? To answer this question, we draw from recent reviews of research on high-dosage tutoring , summer learning programs , reductions in class size , and extending the school day (specifically for literacy instruction) . We report effect sizes for each intervention specific to a grade span and subject wherever possible (e.g., tutoring has been found to have larger effects in elementary math than in reading).

Figure 1 shows the standardized drops in math test scores between students testing in fall 2019 and fall 2021 (separately by elementary and middle school grades) relative to the average effect size of various educational interventions. The average effect size for math tutoring matches or exceeds the average COVID-19 score drop in math. Research on tutoring indicates that it often works best in younger grades, and when provided by a teacher rather than, say, a parent. Further, some of the tutoring programs that produce the biggest effects can be quite intensive (and likely expensive), including having full-time tutors supporting all students (not just those needing remediation) in one-on-one settings during the school day. Meanwhile, the average effect of reducing class size is negative but not significant, with high variability in the impact across different studies. Summer programs in math have been found to be effective (average effect size of .10 SDs), though these programs in isolation likely would not eliminate the COVID-19 test-score drops.

Figure 1: Math COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) Table 2; summer program results are pulled from Lynch et al (2021) Table 2; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span; Figles et al. and Lynch et al. report an overall effect size across elementary and middle grades. We were unable to find a rigorous study that reported effect sizes for extending the school day/year on math performance. Nictow et al. and Kraft & Falken (2021) also note large variations in tutoring effects depending on the type of tutor, with larger effects for teacher and paraprofessional tutoring programs than for nonprofessional and parent tutoring. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

Figure 2 displays a similar comparison using effect sizes from reading interventions. The average effect of tutoring programs on reading achievement is larger than the effects found for the other interventions, though summer reading programs and class size reduction both produced average effect sizes in the ballpark of the COVID-19 reading score drops.

Figure 2: Reading COVID-19 test-score drops compared to the effect sizes of various educational interventions

Source: COVID-19 score drops are pulled from Kuhfeld et al. (2022) Table 5; extended-school-day results are from Figlio et al. (2018) Table 2; reduction-in-class-size results are from pg. 10 of Figles et al. (2018) ; summer program results are pulled from Kim & Quinn (2013) Table 3; and tutoring estimates are pulled from Nictow et al (2020) Table 3B. Ninety-five percent confidence intervals are shown with vertical lines on each bar.

Notes: While Kuhfeld et al. and Nictow et al. reported effect sizes separately by grade span, Figlio et al. and Kim & Quinn report an overall effect size across elementary and middle grades. Class-size reductions included in the Figles meta-analysis ranged from a minimum of one to minimum of eight students per class.

There are some limitations of drawing on research conducted prior to the pandemic to understand our ability to address the COVID-19 test-score drops. First, these studies were conducted under conditions that are very different from what schools currently face, and it is an open question whether the effectiveness of these interventions during the pandemic will be as consistent as they were before the pandemic. Second, we have little evidence and guidance about the efficacy of these interventions at the unprecedented scale that they are now being considered. For example, many school districts are expanding summer learning programs, but school districts have struggled to find staff interested in teaching summer school to meet the increased demand. Finally, given the widening test-score gaps between low- and high-poverty schools, it’s uncertain whether these interventions can actually combat the range of new challenges educators are facing in order to narrow these gaps. That is, students could catch up overall, yet the pandemic might still have lasting, negative effects on educational equality in this country.

Given that the current initiatives are unlikely to be implemented consistently across (and sometimes within) districts, timely feedback on the effects of initiatives and any needed adjustments will be crucial to districts’ success. The Road to COVID Recovery project and the National Student Support Accelerator are two such large-scale evaluation studies that aim to produce this type of evidence while providing resources for districts to track and evaluate their own programming. Additionally, a growing number of resources have been produced with recommendations on how to best implement recovery programs, including scaling up tutoring , summer learning programs , and expanded learning time .

Ultimately, there is much work to be done, and the challenges for students, educators, and parents are considerable. But this may be a moment when decades of educational reform, intervention, and research pay off. Relying on what we have learned could show the way forward.

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COVID-19 and its impact on education, social life and mental health of students: A survey

Affiliation.

• 1 Biometric Research Laboratory, Department of Information Technology, Delhi Technological University, Bawana Road, Delhi 110042, India.
• PMID: 33390636
• PMCID: PMC7762625
• DOI: 10.1016/j.childyouth.2020.105866

The outbreak of COVID-19 affected the lives of all sections of society as people were asked to self-quarantine in their homes to prevent the spread of the virus. The lockdown had serious implications on mental health, resulting in psychological problems including frustration, stress, and depression. In order to explore the impacts of this pandemic on the lives of students, we conducted a survey of a total of 1182 individuals of different age groups from various educational institutes in Delhi - National Capital Region (NCR), India. The article identified the following as the impact of COVID-19 on the students of different age groups: time spent on online classes and self-study, medium used for learning, sleeping habits, daily fitness routine, and the subsequent effects on weight, social life, and mental health. Moreover, our research found that in order to deal with stress and anxiety, participants adopted different coping mechanisms and also sought help from their near ones. Further, the research examined the student's engagement on social media platforms among different age categories. This study suggests that public authorities should take all the necessary measures to enhance the learning experience by mitigating the negative impacts caused due to the COVID-19 outbreak.

Keywords: Children and Youth; Covid-19; Impact; Mental health; Online education; Students.

© 2020 Elsevier Ltd. All rights reserved.

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ORIGINAL RESEARCH article

Teachers and students perspectives on the impact online classroom during the covid-19 pandemic provisionally accepted.

• 1 Walailak University, Thailand

The final, formatted version of the article will be published soon.

During the COVID-19 pandemic, transformation from face-to-face classrooms to online classrooms took place in higher education. This study aims to evaluate the impact of teaching and learning of this change on students' and teachers' perspectives. The study also investigated the various impact factors that hindered online teaching and learning during the COVID-19 lockdown period. Three hundred and eighty-three students and eighty-three teachers at Walailak University, Thailad who had at least three months' experience with online learning and teaching were asked to complete questionnaires that asked about their perspectives with respect to their online classroom experience. The student questionnaire asked about the quality of teachers, quality of communication, the student's work, student's concentration, and satisfaction with the online classroom experience. The teacher questionnaire asked about the teacher's perspective with respect to how the restrictions of online teaching affected teachers, students, and course. The relations between students and teachers were analyzed by chi-square test using the SPSS program version 25.0, with defined p-value as < 0.05 and < 0.10 of student and teacher perspectives respectively. Relations were found between student GPA and student work and student concentration in online classrooms. The teachers' perspective with respect to online teaching was found to be related to the restrictions that online teaching placed on teachers, students, and courses management. The results of the study show teachers' awareness about restrictions that online teaching placed on teachers, students and courses management. Institutions should prioritize and practice using educational technologies at scale, install ICT infrastructure, and engage in lifelong learning.

Keywords: student perspectives, teacher perspectives, Online classroom, student satisfaction, restrictions of online teaching

Received: 08 Nov 2023; Accepted: 16 Apr 2024.

Copyright: © 2024 Rattanawan and Pakdee. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Miss. Sirinthip Pakdee, Walailak University, Tha Sala District, Thailand

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• Published: 16 June 2020

COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research

• Debra L. Weiner 1 , 2 ,
• Vivek Balasubramaniam 3 ,
• Shetal I. Shah 4 &
• Joyce R. Javier 5 , 6

on behalf of the Pediatric Policy Council

Pediatric Research volume  88 ,  pages 148–150 ( 2020 ) Cite this article

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The COVID-19 pandemic has resulted in unprecedented research worldwide. The impact on research in progress at the time of the pandemic, the importance and challenges of real-time pandemic research, and the importance of a pediatrician-scientist workforce are all highlighted by this epic pandemic. As we navigate through and beyond this pandemic, which will have a long-lasting impact on our world, including research and the biomedical research enterprise, it is important to recognize and address opportunities and strategies for, and challenges of research and strengthening the pediatrician-scientist workforce.

The first cases of what is now recognized as SARS-CoV-2 infection, termed COVID-19, were reported in Wuhan, China in December 2019 as cases of fatal pneumonia. By February 26, 2020, COVID-19 had been reported on all continents except Antarctica. As of May 4, 2020, 3.53 million cases and 248,169 deaths have been reported from 210 countries. 1

Impact of COVID-19 on ongoing research

The impact on research in progress prior to COVID-19 was rapid, dramatic, and no doubt will be long term. The pandemic curtailed most academic, industry, and government basic science and clinical research, or redirected research to COVID-19. Most clinical trials, except those testing life-saving therapies, have been paused, and most continuing trials are now closed to new enrollment. Ongoing clinical trials have been modified to enable home administration of treatment and virtual monitoring to minimize participant risk of COVID-19 infection, and to avoid diverting healthcare resources from pandemic response. In addition to short- and long-term patient impact, these research disruptions threaten the careers of physician-scientists, many of whom have had to shift efforts from research to patient care. To protect research in progress, as well as physician-scientist careers and the research workforce, ongoing support is critical. NIH ( https://grants.nih.gov/policy/natural-disasters/corona-virus.htm ), PCORI ( https://www.pcori.org/funding-opportunities/applicant-and-awardee-faqs-related-covid-19 ), and other funders acted swiftly to provide guidance on proposal submission and award management, and implement allowances that enable grant personnel to be paid and time lines to be relaxed. Research institutions have also implemented strategies to mitigate the long-term impact of research disruptions. Support throughout and beyond the pandemic to retain currently well-trained research personnel and research support teams, and to accommodate loss of research assets, including laboratory supplies and study participants, will be required to complete disrupted research and ultimately enable new research.

In the long term, it is likely that the pandemic will force reallocation of research dollars at the expense of research areas funded prior to the pandemic. It will be more important than ever for the pediatric research community to engage in discussion and decisions regarding prioritization of funding goals for dedicated pediatric research and meaningful inclusion of children in studies. The recently released 2020 National Institute of Child Health and Development (NICHD) strategic plan that engaged stakeholders, including scientists and patients, to shape the goals of the Institute, will require modification to best chart a path toward restoring normalcy within pediatric science.

COVID-19 research

This global pandemic once again highlights the importance of research, stable research infrastructure, and funding for public health emergency (PHE)/disaster preparedness, response, and resiliency. The stakes in this worldwide pandemic have never been higher as lives are lost, economies falter, and life has radically changed. Ultimate COVID-19 mitigation and crisis resolution is dependent on high-quality research aligned with top priority societal goals that yields trustworthy data and actionable information. While the highest priority goals are treatment and prevention, biomedical research also provides data critical to manage and restore economic and social welfare.

Scientific and technological knowledge and resources have never been greater and have been leveraged globally to perform COVID-19 research at warp speed. The number of studies related to COVID-19 increases daily, the scope and magnitude of engagement is stunning, and the extent of global collaboration unprecedented. On January 5, 2020, just weeks after the first cases of illness were reported, the genetic sequence, which identified the pathogen as a novel coronavirus, SARS-CoV-2, was released, providing information essential for identifying and developing treatments, vaccines, and diagnostics. As of May 3, 2020 1133 COVID-19 studies, including 148 related to hydroxychloroquine, 13 to remdesivir, 50 to vaccines, and 100 to diagnostic testing, were registered on ClinicalTrials.gov, and 980 different studies on the World Health Organization’s International Clinical Trials Registry Platform (WHO ICTRP), made possible, at least in part, by use of data libraries to inform development of antivirals, immunomodulators, antibody-based biologics, and vaccines. On April 7, 2020, the FDA launched the Coronavirus Treatment Acceleration Program (CTAP) ( https://www.fda.gov/drugs/coronavirus-covid-19-drugs/coronavirus-treatment-acceleration-program-ctap ). On April 17, 2020, NIH announced a partnership with industry to expedite vaccine development ( https://www.nih.gov/news-events/news-releases/nih-launch-public-private-partnership-speed-covid-19-vaccine-treatment-options ). As of May 1, 2020, remdesivir (Gilead), granted FDA emergency use authorization, is the only approved therapeutic for COVID-19. 2

The pandemic has intensified research challenges. In a rush for data already thousands of manuscripts, news reports, and blogs have been published, but to date, there is limited scientifically robust data. Some studies do not meet published clinical trial standards, which now include FDA’s COVID-19-specific standards, 3 , 4 , 5 and/or are published without peer review. Misinformation from studies diverts resources from development and testing of more promising therapeutic candidates and has endangered lives. Ibuprofen, initially reported as unsafe for patients with COVID-19, resulted in a shortage of acetaminophen, endangering individuals for whom ibuprofen is contraindicated. Hydroxychloroquine initially reported as potentially effective for treatment of COVID-19 resulted in shortages for patients with autoimmune diseases. Remdesivir, in rigorous trials, showed decrease in duration of COVID-19, with greater effect given early. 6 Given the limited availability and safety data, the use outside clinical trials is currently approved only for severe disease. Vaccines typically take 10–15 years to develop. As of May 3, 2020, of nearly 100 vaccines in development, 8 are in trial. Several vaccines are projected to have emergency approval within 12–18 months, possibly as early as the end of the year, 7 still an eternity for this pandemic, yet too soon for long-term effectiveness and safety data. Antibody testing, necessary for diagnosis, therapeutics, and vaccine testing, has presented some of the greatest research challenges, including validation, timing, availability and prioritization of testing, interpretation of test results, and appropriate patient and societal actions based on results. 8 Relaxing physical distancing without data regarding test validity, duration, and strength of immunity to different strains of COVID-19 could have catastrophic results. Understanding population differences and disparities, which have been further exposed during this pandemic, is critical for response and long-term pandemic recovery. The “Equitable Data Collection and Disclosure on COVID-19 Act” calls for the CDC (Centers for Disease Control and Prevention) and other HHS (United States Department of Health & Human Services) agencies to publicly release racial and demographic information ( https://bass.house.gov/sites/bass.house.gov/files/Equitable%20Data%20Collection%20and%20Dislosure%20on%20COVID19%20Act_FINAL.pdf )

Trusted sources of up-to-date, easily accessible information must be identified (e.g., WHO https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov , CDC https://www.cdc.gov/coronavirus/2019-nCoV/hcp/index.html , and for children AAP (American Academy of Pediatrics) https://www.aappublications.org/cc/covid-19 ) and should comment on quality of data and provide strategies and crisis standards to guide clinical practice.

Long-term, lessons learned from research during this pandemic could benefit the research enterprise worldwide beyond the pandemic and during other PHE/disasters with strategies for balancing multiple novel approaches and high-quality, time-efficient, cost-effective research. This challenge, at least in part, can be met by appropriate study design, collaboration, patient registries, automated data collection, artificial intelligence, data sharing, and ongoing consideration of appropriate regulatory approval processes. In addition, research to develop and evaluate innovative strategies and technologies to improve access to care, management of health and disease, and quality, safety, and cost effectiveness of care could revolutionize healthcare and healthcare systems. During PHE/disasters, crisis standards for research should be considered along with ongoing and just-in-time PHE/disaster training for researchers willing to share information that could be leveraged at time of crisis. A dedicated funded core workforce of PHE/disaster researchers and funded infrastructure should be considered, potentially as a consortium of networks, that includes physician-scientists, basic scientists, social scientists, mental health providers, global health experts, epidemiologists, public health experts, engineers, information technology experts, economists and educators to strategize, consult, review, monitor, interpret studies, guide appropriate clinical use of data, and inform decisions regarding effective use of resources for PHE/disaster research.

Differences between adult and pediatric COVID-19, the need for pediatric research

As reported by the CDC, from February 12 to April 2, 2020, of 149,760 cases of confirmed COVID-19 in the United States, 2572 (1.7%) were children aged <18 years, similar to published rates in China. 9 Severe illness has been rare. Of 749 children for whom hospitalization data is available, 147 (20%) required hospitalization (5.7% of total children), and 15 of 147 required ICU care (2.0%, 0.58% of total). Of the 95 children aged <1 year, 59 (62%) were hospitalized, and 5 (5.3%) required ICU admission. Among children there were three deaths. Despite children being relatively spared by COVID-19, spread of disease by children, and consequences for their health and pediatric healthcare are potentially profound with immediate and long-term impact on all of society.

We have long been aware of the importance and value of pediatric research on children, and society. COVID-19 is no exception and highlights the imperative need for a pediatrician-scientist workforce. Understanding differences in epidemiology, susceptibility, manifestations, and treatment of COVID-19 in children can provide insights into this pathogen, pathogen–host interactions, pathophysiology, and host response for the entire population. Pediatric clinical registries of COVID-infected, COVID-exposed children can provide data and specimens for immediate and long-term research. Of the 1133 COVID-19 studies on ClinicalTrials.gov, 202 include children aged ≤17 years. Sixty-one of the 681 interventional trials include children. With less diagnostic testing and less pediatric research, we not only endanger children, but also adults by not identifying infected children and limiting spread by children.

Pediatric considerations and challenges related to treatment and vaccine research for COVID-19 include appropriate dosing, pediatric formulation, and pediatric specific short- and long-term effectiveness and safety. Typically, initial clinical trials exclude children until safety has been established in adults. But with time of the essence, deferring pediatric research risks the health of children, particularly those with special needs. Considerations specific to pregnant women, fetuses, and neonates must also be addressed. Childhood mental health in this demographic, already struggling with a mental health pandemic prior to COVID-19, is now further challenged by social disruption, food and housing insecurity, loss of loved ones, isolation from friends and family, and exposure to an infodemic of pandemic-related information. Interestingly, at present mental health visits along with all visits to pediatric emergency departments across the United States are dramatically decreased. Understanding factors that mitigate and worsen psychiatric symptoms should be a focus of research, and ideally will result in strategies for prevention and management in the long term, including beyond this pandemic. Social well-being of children must also be studied. Experts note that the pandemic is a perfect storm for child maltreatment given that vulnerable families are now socially isolated, facing unemployment, and stressed, and that children are not under the watch of mandated reporters in schools, daycare, and primary care. 10 Many states have observed a decrease in child abuse reports and an increase in severity of emergency department abuse cases. In the short term and long term, it will be important to study the impact of access to care, missed care, and disrupted education during COVID-19 on physical and cognitive development.

Training and supporting pediatrician-scientists, such as through NIH physician-scientist research training and career development programs ( https://researchtraining.nih.gov/infographics/physician-scientist ) at all stages of career, as well as fostering research for fellows, residents, and medical students willing to dedicate their research career to, or at least understand implications of their research for, PHE/disasters is important for having an ongoing, as well as a just-in-time surge pediatric-focused PHE/disaster workforce. In addition to including pediatric experts in collaborations and consortiums with broader population focus, consideration should be given to pediatric-focused multi-institutional, academic, industry, and/or government consortiums with infrastructure and ongoing funding for virtual training programs, research teams, and multidisciplinary oversight.

The impact of the COVID-19 pandemic on research and research in response to the pandemic once again highlights the importance of research, challenges of research particularly during PHE/disasters, and opportunities and resources for making research more efficient and cost effective. New paradigms and models for research will hopefully emerge from this pandemic. The importance of building sustained PHE/disaster research infrastructure and a research workforce that includes training and funding for pediatrician-scientists and integrates the pediatrician research workforce into high-quality research across demographics, supports the pediatrician-scientist workforce and pipeline, and benefits society.

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Department of Pediatrics, Division of Emergency Medicine, Boston Children’s Hospital, Boston, MA, USA

Debra L. Weiner

Harvard Medical School, Boston, MA, USA

Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA

Vivek Balasubramaniam

Department of Pediatrics and Division of Neonatology, Maria Fareri Children’s Hospital at Westchester Medical Center, New York Medical College, Valhalla, NY, USA

Shetal I. Shah

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Joyce R. Javier

Keck School of Medicine, University of Southern California, Los Angeles, CA, USA

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Pediatric Policy Council

Scott C. Denne, MD, Chair, Pediatric Policy Council; Mona Patel, MD, Representative to the PPC from the Academic Pediatric Association; Jean L. Raphael, MD, MPH, Representative to the PPC from the Academic Pediatric Association; Jonathan Davis, MD, Representative to the PPC from the American Pediatric Society; DeWayne Pursley, MD, MPH, Representative to the PPC from the American Pediatric Society; Tina Cheng, MD, MPH, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Michael Artman, MD, Representative to the PPC from the Association of Medical School Pediatric Department Chairs; Shetal Shah, MD, Representative to the PPC from the Society for Pediatric Research; Joyce Javier, MD, MPH, MS, Representative to the PPC from the Society for Pediatric Research.

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Weiner, D.L., Balasubramaniam, V., Shah, S.I. et al. COVID-19 impact on research, lessons learned from COVID-19 research, implications for pediatric research. Pediatr Res 88 , 148–150 (2020). https://doi.org/10.1038/s41390-020-1006-3

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Global Commodity Chains and the Pandemic

Labor-power in agricultural sectors in kenya and chile.

• Lara M. Espeter Technische Universität Berlin
• Patricia Retamal University of Chile, Chile

The availability of labor-power is a critical element of all commodity chains. This is especially true of labor-intensive production processes such as agriculture. The COVID-19 pandemic had a major impact on this, as well as on many other aspects of the economy and everyday life. The institutions of the modern world-system responded in various ways to the new situation influenced by COVID-19, taking measures to mitigate and avert the detrimental effects. This paper examines these responses and their impact on the availability of labor-power in the agricultural areas of Nakuru County, Kenya, and O’Higgins Region, Chile. By practically applying world-systems analysis, we shed light on the significance of institutions during periods of stagnation and their impact on the availability of labor-power in global commodity chains. This allows us to draw conclusions about the general impact of institutional responses to stagnation phases at the worker level. We show that the institutions studied responded in very different ways to the stagnation phase affected by COVID-19. As a result, O’Higgins Region experienced a labor-power shortage that Nakuru County had not, which may have a lasting impact on labor-power availability.

Author Biographies

Lara m. espeter, technische universität berlin.

Lara M. Espeter is a research associate in the project Apples and Flowers. Effects of Pandemics on the (Re-)Organization of Commodity Chains for Fresh Agricultural Products and an associate member at the Collaborate Research Centre Re-Figuration of Spaces (CRC 1265) at Technische Universität Berlin. Her research focuses on the origins and effects of social inequality by looking at current and historical structures of the world-economy.

Patricia Retamal, University of Chile, Chile

Patricia Retamal is a PhD candidate in the Territory, Space and Society program at the University of Chile and a thesis student in the regular Fondecyt program (N° 1210331) Extractive citizenships? Citizen practices in rural territories . She is currently the Gender Coordinator of the Vice-Rectory of Research and Development at the University of Chile. Her research focuses on the effects of agribusiness on the social reproduction of women in the workforce.

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Impact of COVID-19: a particular focus on Indian education system

Pushpa gothwal.

1 Amity School of Enginnering and Technology, Amity University Rajasthan, Jaipur, India

Bosky Dharmendra Sharma

2 Mayoor Private School, Abu Dhabi, United Arab Emirates

Nandita Chaube

3 Gujarat Forensic Sciences University, Gandhinagar, India

Nadeem Luqman

4 Ansal University, New Delhi, India

The COVID-19 pandemic has stirred up the world, and its overwhelming impacts can be seen from micro to macro level, that is, from an individual’s day-to-day functioning to the broader level—health sector, finance sector, and off course, the education sector. The younger generation is considered to be the torchbearer of the society. As such, their nutrition, health, safety, and providing education for a holistic development being basic essential needs should be a prime concern for policymakers and all nations worldwide. The present theoretical framework sheds light on the negative as well as the positive impact of COVID-19 on education. It presents a critical analysis of how the education sector experienced a shift from contact teaching to digital learning and got a boost through various online platforms despite having its limitations at the same time including the multidimensional impact of uncertainty and difficulties in sustaining. The chapter also emphasizes the effects of home confinements on students and teachers as well. In this way, the present chapter puts forth the pros and cons of online teaching including various other related aspects.

12.1. Introduction

The word COVID was first discovered in Ontario Cancer Institute in Toronto in 1963. Since then, various mutations were found in different parts of the world, but COVID-19, which was discovered toward the end of 2019, will be written in the history of 2020. The history indicates such incidences every 100 years. Various types of flu infections such as plague (1720), cholera (1817), Spanish flu (1918), and corona virus (2019) have been declared as pandemics. The diagonals of impact or the crater created due to the situation are major concerns today.

The novel corona virus (COVID-19) was declared a pandemic by the World Health Organization (WHO) on March 11, 2020. It is established that this virus influences the aged persons more ( Zhou et al., 2020 ); however, this view was countered ( Bhatnagar et al., 2020 ) and few others have done descriptive and mathematical analysis of COVID spread and made few predictions upon it which are to be observed ( Harjule et al., 2020 , Kumari et al., 2020 , Singh et al., 2020 ). It has globally impacted many sectors like small and large scale businesses, the world economy, health sector, transportation, wages, industries, education, etc. It is evident by the news reports and other reliable sources that this pandemic has majorly brought adverse consequences. However, it is evident that during the global lockdown, a lot of curricular activities, including regular courses, webinars, faculty development programs, lectures, training, and certification programs, have much flourished when it comes to the education sector. Where this online facility has made education easier and comfortable, it has its limitations also. Here, we have emphasized the impact of COVID-19 on the education sector. As per the UNESCO report, the worldwide lockdown has affected over 91% of the world’s student population ( UNESCO, 2019 ). This estimation predicts that the corona virus will adversely impact over 290 million students across 22 countries. The same report estimates that about 32 crore students are affected in India, including those in schools and colleges.

In this chapter we discuss the impact of COVID-19 with a particular focus on education. This chapter is organized as follows: Section 12.1 is introduction; Section 12.2 throws light on impact of COVID-19 on education, which has two subsections—effect of home confinement on children and teachers, and a multidimensional impact of uncertainty. Section 12.3 describes sustaining the education industry during COVID-19 and conclusions are mentioned in the last section.

12.2. Impact of COVID-19 on education

During this pandemic education sector has experienced gross changes such as a shift from regular contact classes to online platforms, modified teaching pedagogy adopted by teachers, conduction of examinations and competitive exams etc. As per the UNESCO report in the education sector, 1,190,287,189 learners have been affected and 150 countrywide closures ( UNESCO, 2019 ). The effect of COVID-19 on the education and mental health of students and academic staff has been explored in the studies ( Cao et al., 2020 , Sahu, 2020 ). It presents some challenges due to COVID-19 on education. First, to protect the traditional teaching system, which is entirely shifted to online teaching, which requires teachers’ training, strong technical support, and high-speed internet, which is not accessible for everyone. Second, the assessment and evaluation system using an online platform does not provide student performance accuracy because the originality of performance cannot be assured ( Ruder, 2019 ). The students may use some other device to take help while answering the questions asked during the assessment. The third is the research platform, including international travel, cancellation, and postponing conferences and seminars. Other research activities have adversely affected the work ( Hutton, Dudley, Horowitz-Kraus, DeWitt, & Holland, 2020 ). However, many such events have shifted to online platforms based on the possibilities, which has increased the participation and popularity of these events ( Cao et al., 2020 ). The fourth concern is student mental health and career, which is grossly affected due to this outbreak ( Sahu, 2020 ).

Studies have been conducted where the impact of COVID-19 on physicians’ education was to be assessed for which they conducted seminars based on self-regulation theory and found significant results ( Clark et al., 1998 , Ferrel and Ryan, 2020 ). Ferral and Ahmad discussed the pandemic’s impact due to which some hospitals in the United Kingdom canceled students’ internship and observations ( Ahmed et al., 2020 , Ferrel and Ryan, 2020 ). This was reassured by another study, which concluded that, as a preventive measure, many hospitals are not permitting students in hospitals, which is adversely influencing their education ( Burgess & Sievertsen, 2020 ).

Edgar discussed the effects of COVID-19 on higher secondary education and the impact of using Science Technology Engineering and Mathematics education. In this study, the authors collected data through the telephonic mode from public school teachers, where they found a significant drop in these students’ academic performance ( Iyer, Aziz, & Ojcius, 2020 ).

COVID-19 has brought the entire education methods from traditional to online modes. There are various online platforms available for learners and professionals. The students can work with peace of mind while staying at their homes where their time, energy, and money are not wasted traveling. They are not fatigued and hence can invest themselves more in comparison to preCOVID conditions. Studying at home has also provided a more significant benefit to the students being directly monitored by parents. When it comes to theory classes, the online platform has given them a vast chance to excel. However, the practical assignments that the students are supposed to conduct in laboratories and fields have seen a major constraint. This has created a significant limitation of teaching for teachers when they cannot provide the demonstrations to the students in the absence of laboratory instruments and other necessary practical materials.

However, this has led to the timely completion of courses despite the complete lockdown but with incomplete knowledge among students whose courses are more practical. Therefore a combination of these pros and cons has brought the education world to a different level.

Several online platforms are available for lectures, training etc., which have made learning easier ( Bambakidis & Tomei, 2020 ). However, in the absence of contact teaching, a one-to-one discussion between a teacher and students is adversely influenced. The chances of filling this lacuna are also not assured because the students will probably be deprived of contact learning before being promoted to the next level. This again leads to next level difficulties that these students may face shortly soon due to unclear concepts of previous standards/grades ( Sintema, 2020 ). Also, in the absence of a formal class environment, the student’s concentration is more likely to be adversely influenced.

Where the online facility has provided the ease of learning through flexi classes, there is no surety that the student himself or herself is attending the class. Due to network troubles, sometimes the teacher and students face many disturbances. Students sometimes get involved in mischievous activities by making fake email IDs, making noises, or giving unnecessary comments etc. The teacher faces difficulty maintaining discipline. However, this online mode is more appropriate for some disciplines than direct contact teachings, such as web designing, etc., where the practical demonstration can be better understood through online presentation and screen sharing options.

On the other hand, students from the low socio-economic class are getting no chance to experience online learning. This creates a huge and unfair social stratification where learners are left deprived of their legitimate right to education. In developing countries like India, where a huge population belongs to rural backgrounds, people are not so technology friendly. This is another challenge for the Indian education system despite the availability of technological facilities. This difficulty is faced by either or both teacher and student. Teachers who are more apt and comfortable in contact teaching cannot give their 100% through online lectures. A very advantageous and constructive aspect that emerged during the lockdown is that many professionals started throwing free online courses, training programs, workshops, webinars, etc., which have given a good chance to all the learners to update their credentials at no expense. People having busy official schedules who are usually not able to invest time in such programs are now getting a chance to upgrade themselves. On the other hand, young professionals are getting a fair chance to present themselves with more confidence.

When it comes to the physiological and cognitive effects, online education has both advantages and disadvantages. Recently, a study was conducted at Harvard Medical School on digital devices’ interference in sleep and creativity. It was found that the use of digital media plays a significant role in making the neural connection for a growing human brain. However, the screen usage of more than the recommended hours can lead to lower brain development. This also leads to the disruption of sleep by undersecretion of the melatonin hormone.

Another major concern is the availability of study resources. Not all the study material is available through online mode. Several offline materials are usually available in the library but not in the online database. A student is being deprived of this material. Furthermore, the educational institutions, which have decided to conduct online examinations, face difficulty in preparing question papers. The question papers are mostly multiple choices that do not give the student a window to write descriptive answers, which are equally crucial for a student to learn. This improves the writing skills of the student.

Where the online conduct of classes and conducting examinations has its challenges, the evaluation, on the other hand, has become more convenient and transparent between the teacher and student, where the students come to know about their performance. There are platforms that allow the faculty to give online assignments and evaluation. Online teaching does not require a large infrastructure for the conduct of classes. Instead, a strong IT team is sufficient to make it workable. In direct contact teaching, the other teaching and stationary materials are required, in the absence of which teaching is likely to suffer. The online teaching platform has covered up this drawback of direct contact teaching. However, online teaching makes people more digitally dependent by reducing direct and one-to-one social interaction. This is gradually making people more technology addicts.

12.2.1. Effect of home confinement on children and teachers

Due to the COVID-19 crisis (in more than 150 countries), all levels of the education system, from preschool to tertiary education, have been affected ( Bjorklund and Salvanes, 2011 , Vahid, 2020 ), wherein gradual closure of schools and universities took place. Similar situations prevailed in the past as well, during the pandemics ( Klaiman, Kraemer, & Stoto, 2011 ). Being confined to home or lockdown has impacted lives and livelihood across different spheres and so the education sector too, though have been able to meet the demands ensuring that via “online learning,” “homeschooling,” “virtual learning,” or “E-learning” children’s educational attainment remains undisrupted mainly ( IAU, 2020 ).

At the tertiary level, almost all universities and colleges have offered online courses and switched to virtual lectures, classes, and webinars ( Strielkowski, 2020 ), since digital learning has emerged as a significant aid for education from just an extracurricular facility. Although the contingencies of digital technologies rendition go past a stop-gap solution during the crisis, it has helped answer a new set of questions entirely about what, how, where, and when students shall learn. With the help of technology, students and teachers can ingress resource materials and not limit just to the text books in different formats, styles at their own pace and time by just going online. Besides teachers, smart digital technologies do not just teach only. Instead, it simultaneously observes, monitors how we study, how we learn, what interests us, the tasks that we involve in, the kind of problems that we face and find difficult to solve and adapt accordingly to meet the needs of the learner with more accuracy, specifications as compared to traditional learning within classrooms ( Kumar, 2020 ).

However, the necessary measures taken are highly applaudable; there are various issues that arise due to prolonged school closures and home confinement ( Cao et al., 2020 ) impacting students’ well-being in COVID time wherein students feel physically less active, sleep irregularities, dietary changes marked by weight gain along with low motivation ( Wickens, 2011 ), boredom to getting more anxious, and irritable as well. Abundant research has been carried out, suggesting having adverse effects on physical and psychological health in school-going children and students pursuing higher education at colleges and universities ( Liu et al., 2019 ). Nevertheless, at the tertiary level, the closing of campuses left them with no choice to leave hostels and dormitories and return to their hometown; however, many got stuck too, leaving them helpless and anxious ( Grubic, Badovinac, & Johri, 2020 ).

The switch to online education ensures minimum loss of studies suffered, and progress and attainment are also closely monitored via timely assessment and evaluations. Internal learning evaluation and assessments are considered to have high significance as it demonstrates the students’ learning needs and support for taking remedial actions ( Pandit, 2020 ). However, having been shifted to online platforms and accessed remotely, a major concern that emerged was the availability of proper internet facility networks and technology, especially in lower socio-economic zones and strata. In many countries, via online portals, TV and radio channels were started and the concern was addressed by the respective governments ( Gyamerah, 2020 ).

Imparting of average grade points based on the course completion for students pursuing higher studies, deferring the exams till further notice, promotion to the next level using “predictive grade,” were announced by few higher education institutions and schools. As per Gonzalez et al. (2020) and Black and Wiliam (2018) , the evaluation method and assessment would also change from traditional high stake to small project-based and activity, assignment-based evaluation shortly as the pandemic continues. At higher education institutes, there is a hold on the ongoing research projects and field works. A virtual internship is provided and various scientific research conferences and symposiums have been postponed and canceled ( Viner et al., 2020 ). They have moved online, whereby these virtual conferences have adversely affected networking opportunities and informal communication, creating a wide gap, especially in case of the inequalities prevailed in accessing technology to educational resources and the absence of proper remedial measures ( Gjoshi & Kume, 2014 ).

It is perceived that higher education can be relatively managed with digital learning or remote schooling ( Srivastava, 2020 ). As such, most of the research carried out to study the effect of the COVID-19 pandemic on education discusses the adverse effects in terms of learning and student well-being ( Herold, 2017 ) due to home confinement and digital learning or homeschooling taking place with parental issues and concerns to provide childcare management and guidance required for their distance learning programs, availability of resources, and their socio-economic conditions ( Hiremath, Kowshik, Manjunath, & Shettar, 2020 ).

Despite the ongoing conditions prevailing due to COVID-19, online learning has said to have long-term positive implications that can be expected in comparison to the earlier research studies that suggested that student well-being is affected by the quality of learning ( Mahboob, 2020 ). A recent study sheds light on the significant positive impact of COVID-19 on learning efficiency and performances by adopting online learning strategies. To better understand the teaching and learning process during this crisis, it is imperative to have an education reform made to provide necessary teacher training, making further advancement of the new normal digital learning for functioning smoothly in the future as well ( Stephens, Leevore, Coryell, & Pena, 2017 ).

Furthermore, according to WHO, COVID-19 may never be gone. Instead, people have to learn to live with it. As such, by the policymakers, distance learning is embedded in normal education, so as to help students learn coping skills to deal effectively, minimizing negative impacts in case of crises encountered.

However, as a need of the hour, education shall increasingly embrace online/virtual classrooms, keeping in mind the exposure to students’ screen time in a day, planning of activities wherein parental involvement, assistance, and guidance are considered ( India Today, 2020 ). More physical education, music, dance, home gardening along with art integration should be focussed so as to enhance creativity, and affective domains that advertently shall enhance motivation, physical activities ( Sprang & Silman, 2013 ) and in adolescence too, continuous sitting, eye strains ( Levy & Ramim, 2017 ) and issues like cyber bullying, video game addictions and social media browsing can be put under control. Even for university students, through distance learning, they can collaborate with others, watch lectures prerecorded, and have fruitful discussions. The lecturer can be more of a facilitator rather than an instructor. Distance learning can be as effective as a traditional face-to-face mode of learning. Students have more family time; they can engage at their own pace ( Simonson, Zvacek, & Smaldino, 2019 ).

Moreover, there are barriers to distance learning and are unique to every country. However, its use has worldwide benefits that can be counted on, especially educating, imparting training on various focussed topics to general hobbies ( Bell et al., 2017 ). For educators, having been faced with so many challenges to adjust and get accustomed to the distance learning platform, it is highly commendable to have done so effectively. Still, they find it convincing, and a feel-good factor also persists, as work from home has helped manage home, take care of one’s self and family as mostly the time is spent on daily commuting, travels to reach the workplace, endless department meetings, colloquia or ongoing discussions on one side, and on the contrary, the research evidence ( Goodman, Joshi, Nasim, & Tyler, 2015 ) demonstrated that parents with a low socio-economic background faced difficulties in providing nutritional meals to their children due to school closures, and also the affordability of extra-school activities compared to more advantaged backgrounds.

Nevertheless, to minimize the challenges experienced due to home confinement and school closures, distance learning should be encouraged. Need for updating with modern technology should be introduced with high-speed internet, continuous power supply, cyber security, as well as proper training to educators and students so as to have skills and competencies to operate electronic devices, along with the necessary knowledge and understanding about the method in which the information is imparted.

Clearly, due to our recent experience with the COVID-19 pandemic, many conventional academic life principles have to be reshaped. However, a common goal is being shared by all the education systems, which is to overcome the learning crisis faced and deal effectively with the COVID-19 pandemic.

While talking about the family environment, it has been observed that many faculties are reporting about online teaching difficulties. Especially in children’s cases, it is reported that the families are not cooperating to maintain the class’s decorum. The family members keep disturbing the child for one or the other reason, which promotes the child to continue with disturbing and inattentive behavior. The cognitive skills of the parents also have a significant role in understanding and growth of the child. If the academic and the other assignments are better understood by the parents, the children will have a constant source of support whenever needed without any delay or waiting time for the next interaction with the teacher. In this aspect, India is facing much difficulty because a large population is illiterate or less educated to compliment the contemporary educational demands of their children. Hence, the family has a central role in the learning of the child ( Moon, Kim, & Moon, 2016 ).

12.2.2. A multidimensional impact of uncertainty

The diagonals of impact or the crater that is created due to the situation is a matter of major concern today. If we see the situation and scenario, we will find that this pandemic problem is not just medical or psychological. However, it encompasses a three-dimensional area, that is the bio-psycho-social domain of health psychology, which explains an interconnection between biology, psychology, and socio-environmental factors. This model plays an important role in defining interaction between humans and the environment and puts light on humans’ interaction with their social environment in which we operate within certain domains and norms. When these domains are affected by environmental factors, a lethal combination takes birth. The world is facing the same evidence in the form of various psychological and socio-environmental outcomes, such as financial, mental health, environmental, etc. None of the areas are untouched by the pandemic influences. In the current chapter, the impacts of COVID-19 on education are explained in detail.

Suppose we see the present scenario when uncertainty is prevailing in every sector of society. In that case, it will not be superlative to say that the students of today, despite having their completed degrees, will have a certain and stable career. Such situations are making the students prone to rumination , which means that they are most likely to think about their uncertain future. In the present context, it is in terms of examination outcome and job security. This thought process is likely to affect their overall psyche and, in turn, will lead to a greater rise in major psychological problems.

12.3. Sustaining the education industry during COVID-19

This pandemic situation generates many education losses like postponing the board exams, competitive exams, government exams, schools and colleges closed, etc. To overcome or minimize these losses, the Human Resource Development (HRD) minister released the guideline to all educational institutes to utilize the online platforms for teaching purposes ( Di Pietro, Biagi, Costa, Karpiński, & Mazza, 2020 ). Here, the most popular open-source of online teaching platforms are MS Team, Moodle, Zoom App, Chamilo, Webex, Canvas, Forms, Google Hangouts, and Google Meet. These platforms have helped teachers in online lecture delivery, sharing of notes, assessment, quiz conduction, etc. Several e-learning platforms are also available for students, which offer free certification or audit of the courses. These sources are Coursera, NPTEL, Swayam, edX, WHO, Harvard University, Stanford University, MITs, IITs, NITs, and many more. Therefore, in this situation, students learn at their own pace using digital platforms, while protecting themselves from the corona virus. Hence, the impact of COVID-19 on the education sector is compensated by online teaching platforms ( UNESCO, 2020 ). This online platform also provides teachers and students with various opportunities to interact with experts as per their area of interest without any expenses. Such teaching facilitates students’ effective utilization of time and more online learning activities based on their preferences.

The entire chapter can be summarized in the table mentioned below:

12.4. Conclusion

The pandemic situation has adversely affected several sectors, but the education sector has had both advantages and disadvantages. The virus outbreak has negatively influenced other areas; the education sector has been able to sustain and has shown its advantages. Especially when we talk about digital education, it has proved to be a savior of the entire education system. However, it cannot be avoided that this digitalization has come up with its limitations. It has its pros and cons, such as home confinement, blocked socialization etc. Hence, in this epidemic situation, the fulfilment of course requirements is majorly satisfied. However, the quality of learning and outcome is adversely affected in some teaching areas, which further opens the door to more advanced education reformed by policymakers and government. Therefore, a futuristic approach to implementing such an education system needs much planning to provide a better learning platform. However, to get better results, online teaching techniques and traditional pedagogy may produce highly productive results. Therefore it can be concluded that despite having limitations, this COVID-19 pandemic has got a boost through various online platforms.

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