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The effects of online education on academic success: A meta-analysis study

• Published: 06 September 2021
• Volume 27 , pages 429–450, ( 2022 )

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• Hakan Ulum   ORCID: orcid.org/0000-0002-1398-6935 1  

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The purpose of this study is to analyze the effect of online education, which has been extensively used on student achievement since the beginning of the pandemic. In line with this purpose, a meta-analysis of the related studies focusing on the effect of online education on students’ academic achievement in several countries between the years 2010 and 2021 was carried out. Furthermore, this study will provide a source to assist future studies with comparing the effect of online education on academic achievement before and after the pandemic. This meta-analysis study consists of 27 studies in total. The meta-analysis involves the studies conducted in the USA, Taiwan, Turkey, China, Philippines, Ireland, and Georgia. The studies included in the meta-analysis are experimental studies, and the total sample size is 1772. In the study, the funnel plot, Duval and Tweedie’s Trip and Fill Analysis, Orwin’s Safe N Analysis, and Egger’s Regression Test were utilized to determine the publication bias, which has been found to be quite low. Besides, Hedge’s g statistic was employed to measure the effect size for the difference between the means performed in accordance with the random effects model. The results of the study show that the effect size of online education on academic achievement is on a medium level. The heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

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1 Introduction

Information and communication technologies have become a powerful force in transforming the educational settings around the world. The pandemic has been an important factor in transferring traditional physical classrooms settings through adopting information and communication technologies and has also accelerated the transformation. The literature supports that learning environments connected to information and communication technologies highly satisfy students. Therefore, we need to keep interest in technology-based learning environments. Clearly, technology has had a huge impact on young people's online lives. This digital revolution can synergize the educational ambitions and interests of digitally addicted students. In essence, COVID-19 has provided us with an opportunity to embrace online learning as education systems have to keep up with the rapid emergence of new technologies.

Information and communication technologies that have an effect on all spheres of life are also actively included in the education field. With the recent developments, using technology in education has become inevitable due to personal and social reasons (Usta, 2011a ). Online education may be given as an example of using information and communication technologies as a consequence of the technological developments. Also, it is crystal clear that online learning is a popular way of obtaining instruction (Demiralay et al., 2016 ; Pillay et al., 2007 ), which is defined by Horton ( 2000 ) as a way of education that is performed through a web browser or an online application without requiring an extra software or a learning source. Furthermore, online learning is described as a way of utilizing the internet to obtain the related learning sources during the learning process, to interact with the content, the teacher, and other learners, as well as to get support throughout the learning process (Ally, 2004 ). Online learning has such benefits as learning independently at any time and place (Vrasidas & MsIsaac, 2000 ), granting facility (Poole, 2000 ), flexibility (Chizmar & Walbert, 1999 ), self-regulation skills (Usta, 2011b ), learning with collaboration, and opportunity to plan self-learning process.

Even though online education practices have not been comprehensive as it is now, internet and computers have been used in education as alternative learning tools in correlation with the advances in technology. The first distance education attempt in the world was initiated by the ‘Steno Courses’ announcement published in Boston newspaper in 1728. Furthermore, in the nineteenth century, Sweden University started the “Correspondence Composition Courses” for women, and University Correspondence College was afterwards founded for the correspondence courses in 1843 (Arat & Bakan, 2011 ). Recently, distance education has been performed through computers, assisted by the facilities of the internet technologies, and soon, it has evolved into a mobile education practice that is emanating from progress in the speed of internet connection, and the development of mobile devices.

With the emergence of pandemic (Covid-19), face to face education has almost been put to a halt, and online education has gained significant importance. The Microsoft management team declared to have 750 users involved in the online education activities on the 10 th March, just before the pandemic; however, on March 24, they informed that the number of users increased significantly, reaching the number of 138,698 users (OECD, 2020 ). This event supports the view that it is better to commonly use online education rather than using it as a traditional alternative educational tool when students do not have the opportunity to have a face to face education (Geostat, 2019 ). The period of Covid-19 pandemic has emerged as a sudden state of having limited opportunities. Face to face education has stopped in this period for a long time. The global spread of Covid-19 affected more than 850 million students all around the world, and it caused the suspension of face to face education. Different countries have proposed several solutions in order to maintain the education process during the pandemic. Schools have had to change their curriculum, and many countries supported the online education practices soon after the pandemic. In other words, traditional education gave its way to online education practices. At least 96 countries have been motivated to access online libraries, TV broadcasts, instructions, sources, video lectures, and online channels (UNESCO, 2020 ). In such a painful period, educational institutions went through online education practices by the help of huge companies such as Microsoft, Google, Zoom, Skype, FaceTime, and Slack. Thus, online education has been discussed in the education agenda more intensively than ever before.

Although online education approaches were not used as comprehensively as it has been used recently, it was utilized as an alternative learning approach in education for a long time in parallel with the development of technology, internet and computers. The academic achievement of the students is often aimed to be promoted by employing online education approaches. In this regard, academicians in various countries have conducted many studies on the evaluation of online education approaches and published the related results. However, the accumulation of scientific data on online education approaches creates difficulties in keeping, organizing and synthesizing the findings. In this research area, studies are being conducted at an increasing rate making it difficult for scientists to be aware of all the research outside of their ​​expertise. Another problem encountered in the related study area is that online education studies are repetitive. Studies often utilize slightly different methods, measures, and/or examples to avoid duplication. This erroneous approach makes it difficult to distinguish between significant differences in the related results. In other words, if there are significant differences in the results of the studies, it may be difficult to express what variety explains the differences in these results. One obvious solution to these problems is to systematically review the results of various studies and uncover the sources. One method of performing such systematic syntheses is the application of meta-analysis which is a methodological and statistical approach to draw conclusions from the literature. At this point, how effective online education applications are in increasing the academic success is an important detail. Has online education, which is likely to be encountered frequently in the continuing pandemic period, been successful in the last ten years? If successful, how much was the impact? Did different variables have an impact on this effect? Academics across the globe have carried out studies on the evaluation of online education platforms and publishing the related results (Chiao et al., 2018 ). It is quite important to evaluate the results of the studies that have been published up until now, and that will be published in the future. Has the online education been successful? If it has been, how big is the impact? Do the different variables affect this impact? What should we consider in the next coming online education practices? These questions have all motivated us to carry out this study. We have conducted a comprehensive meta-analysis study that tries to provide a discussion platform on how to develop efficient online programs for educators and policy makers by reviewing the related studies on online education, presenting the effect size, and revealing the effect of diverse variables on the general impact.

There have been many critical discussions and comprehensive studies on the differences between online and face to face learning; however, the focus of this paper is different in the sense that it clarifies the magnitude of the effect of online education and teaching process, and it represents what factors should be controlled to help increase the effect size. Indeed, the purpose here is to provide conscious decisions in the implementation of the online education process.

The general impact of online education on the academic achievement will be discovered in the study. Therefore, this will provide an opportunity to get a general overview of the online education which has been practiced and discussed intensively in the pandemic period. Moreover, the general impact of online education on academic achievement will be analyzed, considering different variables. In other words, the current study will allow to totally evaluate the study results from the related literature, and to analyze the results considering several cultures, lectures, and class levels. Considering all the related points, this study seeks to answer the following research questions:

What is the effect size of online education on academic achievement?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the country?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the class level?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the lecture?

How do the effect sizes of online education on academic achievement change according to the moderator variable of the online education approaches?

This study aims at determining the effect size of online education, which has been highly used since the beginning of the pandemic, on students’ academic achievement in different courses by using a meta-analysis method. Meta-analysis is a synthesis method that enables gathering of several study results accurately and efficiently, and getting the total results in the end (Tsagris & Fragkos, 2018 ).

2.1 Selecting and coding the data (studies)

The required literature for the meta-analysis study was reviewed in July, 2020, and the follow-up review was conducted in September, 2020. The purpose of the follow-up review was to include the studies which were published in the conduction period of this study, and which met the related inclusion criteria. However, no study was encountered to be included in the follow-up review.

In order to access the studies in the meta-analysis, the databases of Web of Science, ERIC, and SCOPUS were reviewed by utilizing the keywords ‘online learning and online education’. Not every database has a search engine that grants access to the studies by writing the keywords, and this obstacle was considered to be an important problem to be overcome. Therefore, a platform that has a special design was utilized by the researcher. With this purpose, through the open access system of Cukurova University Library, detailed reviews were practiced using EBSCO Information Services (EBSCO) that allow reviewing the whole collection of research through a sole searching box. Since the fundamental variables of this study are online education and online learning, the literature was systematically reviewed in the related databases (Web of Science, ERIC, and SCOPUS) by referring to the keywords. Within this scope, 225 articles were accessed, and the studies were included in the coding key list formed by the researcher. The name of the researchers, the year, the database (Web of Science, ERIC, and SCOPUS), the sample group and size, the lectures that the academic achievement was tested in, the country that the study was conducted in, and the class levels were all included in this coding key.

The following criteria were identified to include 225 research studies which were coded based on the theoretical basis of the meta-analysis study: (1) The studies should be published in the refereed journals between the years 2020 and 2021, (2) The studies should be experimental studies that try to determine the effect of online education and online learning on academic achievement, (3) The values of the stated variables or the required statistics to calculate these values should be stated in the results of the studies, and (4) The sample group of the study should be at a primary education level. These criteria were also used as the exclusion criteria in the sense that the studies that do not meet the required criteria were not included in the present study.

After the inclusion criteria were determined, a systematic review process was conducted, following the year criterion of the study by means of EBSCO. Within this scope, 290,365 studies that analyze the effect of online education and online learning on academic achievement were accordingly accessed. The database (Web of Science, ERIC, and SCOPUS) was also used as a filter by analyzing the inclusion criteria. Hence, the number of the studies that were analyzed was 58,616. Afterwards, the keyword ‘primary education’ was used as the filter and the number of studies included in the study decreased to 3152. Lastly, the literature was reviewed by using the keyword ‘academic achievement’ and 225 studies were accessed. All the information of 225 articles was included in the coding key.

It is necessary for the coders to review the related studies accurately and control the validity, safety, and accuracy of the studies (Stewart & Kamins, 2001 ). Within this scope, the studies that were determined based on the variables used in this study were first reviewed by three researchers from primary education field, then the accessed studies were combined and processed in the coding key by the researcher. All these studies that were processed in the coding key were analyzed in accordance with the inclusion criteria by all the researchers in the meetings, and it was decided that 27 studies met the inclusion criteria (Atici & Polat, 2010 ; Carreon, 2018 ; Ceylan & Elitok Kesici, 2017 ; Chae & Shin, 2016 ; Chiang et al. 2014 ; Ercan, 2014 ; Ercan et al., 2016 ; Gwo-Jen et al., 2018 ; Hayes & Stewart, 2016 ; Hwang et al., 2012 ; Kert et al., 2017 ; Lai & Chen, 2010 ; Lai et al., 2015 ; Meyers et al., 2015 ; Ravenel et al., 2014 ; Sung et al., 2016 ; Wang & Chen, 2013 ; Yu, 2019 ; Yu & Chen, 2014 ; Yu & Pan, 2014 ; Yu et al., 2010 ; Zhong et al., 2017 ). The data from the studies meeting the inclusion criteria were independently processed in the second coding key by three researchers, and consensus meetings were arranged for further discussion. After the meetings, researchers came to an agreement that the data were coded accurately and precisely. Having identified the effect sizes and heterogeneity of the study, moderator variables that will show the differences between the effect sizes were determined. The data related to the determined moderator variables were added to the coding key by three researchers, and a new consensus meeting was arranged. After the meeting, researchers came to an agreement that moderator variables were coded accurately and precisely.

2.2 Study group

27 studies are included in the meta-analysis. The total sample size of the studies that are included in the analysis is 1772. The characteristics of the studies included are given in Table 1 .

2.3 Publication bias

Publication bias is the low capability of published studies on a research subject to represent all completed studies on the same subject (Card, 2011 ; Littell et al., 2008 ). Similarly, publication bias is the state of having a relationship between the probability of the publication of a study on a subject, and the effect size and significance that it produces. Within this scope, publication bias may occur when the researchers do not want to publish the study as a result of failing to obtain the expected results, or not being approved by the scientific journals, and consequently not being included in the study synthesis (Makowski et al., 2019 ). The high possibility of publication bias in a meta-analysis study negatively affects (Pecoraro, 2018 ) the accuracy of the combined effect size, causing the average effect size to be reported differently than it should be (Borenstein et al., 2009 ). For this reason, the possibility of publication bias in the included studies was tested before determining the effect sizes of the relationships between the stated variables. The possibility of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

2.4 Selecting the model

After determining the probability of publication bias of this meta-analysis study, the statistical model used to calculate the effect sizes was selected. The main approaches used in the effect size calculations according to the differentiation level of inter-study variance are fixed and random effects models (Pigott, 2012 ). Fixed effects model refers to the homogeneity of the characteristics of combined studies apart from the sample sizes, while random effects model refers to the parameter diversity between the studies (Cumming, 2012 ). While calculating the average effect size in the random effects model (Deeks et al., 2008 ) that is based on the assumption that effect predictions of different studies are only the result of a similar distribution, it is necessary to consider several situations such as the effect size apart from the sample error of combined studies, characteristics of the participants, duration, scope, and pattern of the study (Littell et al., 2008 ). While deciding the model in the meta-analysis study, the assumptions on the sample characteristics of the studies included in the analysis and the inferences that the researcher aims to make should be taken into consideration. The fact that the sample characteristics of the studies conducted in the field of social sciences are affected by various parameters shows that using random effects model is more appropriate in this sense. Besides, it is stated that the inferences made with the random effects model are beyond the studies included in the meta-analysis (Field, 2003 ; Field & Gillett, 2010 ). Therefore, using random effects model also contributes to the generalization of research data. The specified criteria for the statistical model selection show that according to the nature of the meta-analysis study, the model should be selected just before the analysis (Borenstein et al., 2007 ; Littell et al., 2008 ). Within this framework, it was decided to make use of the random effects model, considering that the students who are the samples of the studies included in the meta-analysis are from different countries and cultures, the sample characteristics of the studies differ, and the patterns and scopes of the studies vary as well.

2.5 Heterogeneity

Meta-analysis facilitates analyzing the research subject with different parameters by showing the level of diversity between the included studies. Within this frame, whether there is a heterogeneous distribution between the studies included in the study or not has been evaluated in the present study. The heterogeneity of the studies combined in this meta-analysis study has been determined through Q and I 2 tests. Q test evaluates the random distribution probability of the differences between the observed results (Deeks et al., 2008 ). Q value exceeding 2 value calculated according to the degree of freedom and significance, indicates the heterogeneity of the combined effect sizes (Card, 2011 ). I 2 test, which is the complementary of the Q test, shows the heterogeneity amount of the effect sizes (Cleophas & Zwinderman, 2017 ). I 2 value being higher than 75% is explained as high level of heterogeneity.

In case of encountering heterogeneity in the studies included in the meta-analysis, the reasons of heterogeneity can be analyzed by referring to the study characteristics. The study characteristics which may be related to the heterogeneity between the included studies can be interpreted through subgroup analysis or meta-regression analysis (Deeks et al., 2008 ). While determining the moderator variables, the sufficiency of the number of variables, the relationship between the moderators, and the condition to explain the differences between the results of the studies have all been considered in the present study. Within this scope, it was predicted in this meta-analysis study that the heterogeneity can be explained with the country, class level, and lecture moderator variables of the study in terms of the effect of online education, which has been highly used since the beginning of the pandemic, and it has an impact on the students’ academic achievement in different lectures. Some subgroups were evaluated and categorized together, considering that the number of effect sizes of the sub-dimensions of the specified variables is not sufficient to perform moderator analysis (e.g. the countries where the studies were conducted).

2.6 Interpreting the effect sizes

Effect size is a factor that shows how much the independent variable affects the dependent variable positively or negatively in each included study in the meta-analysis (Dinçer, 2014 ). While interpreting the effect sizes obtained from the meta-analysis, the classifications of Cohen et al. ( 2007 ) have been utilized. The case of differentiating the specified relationships of the situation of the country, class level, and school subject variables of the study has been identified through the Q test, degree of freedom, and p significance value Fig.  1 and 2 .

3 Findings and results

The purpose of this study is to determine the effect size of online education on academic achievement. Before determining the effect sizes in the study, the probability of publication bias of this meta-analysis study was analyzed by using the funnel plot, Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test.

When the funnel plots are examined, it is seen that the studies included in the analysis are distributed symmetrically on both sides of the combined effect size axis, and they are generally collected in the middle and lower sections. The probability of publication bias is low according to the plots. However, since the results of the funnel scatter plots may cause subjective interpretations, they have been supported by additional analyses (Littell et al., 2008 ). Therefore, in order to provide an extra proof for the probability of publication bias, it has been analyzed through Orwin’s Safe N Analysis, Duval and Tweedie’s Trip and Fill Analysis, and Egger’s Regression Test (Table 2 ).

Table 2 consists of the results of the rates of publication bias probability before counting the effect size of online education on academic achievement. According to the table, Orwin Safe N analysis results show that it is not necessary to add new studies to the meta-analysis in order for Hedges g to reach a value outside the range of ± 0.01. The Duval and Tweedie test shows that excluding the studies that negatively affect the symmetry of the funnel scatter plots for each meta-analysis or adding their exact symmetrical equivalents does not significantly differentiate the calculated effect size. The insignificance of the Egger tests results reveals that there is no publication bias in the meta-analysis study. The results of the analysis indicate the high internal validity of the effect sizes and the adequacy of representing the studies conducted on the relevant subject.

In this study, it was aimed to determine the effect size of online education on academic achievement after testing the publication bias. In line with the first purpose of the study, the forest graph regarding the effect size of online education on academic achievement is shown in Fig.  3 , and the statistics regarding the effect size are given in Table 3 .

The flow chart of the scanning and selection process of the studies

Funnel plot graphics representing the effect size of the effects of online education on academic success

Forest graph related to the effect size of online education on academic success

The square symbols in the forest graph in Fig.  3 represent the effect sizes, while the horizontal lines show the intervals in 95% confidence of the effect sizes, and the diamond symbol shows the overall effect size. When the forest graph is analyzed, it is seen that the lower and upper limits of the combined effect sizes are generally close to each other, and the study loads are similar. This similarity in terms of study loads indicates the similarity of the contribution of the combined studies to the overall effect size.

Figure  3 clearly represents that the study of Liu and others (Liu et al., 2018 ) has the lowest, and the study of Ercan and Bilen ( 2014 ) has the highest effect sizes. The forest graph shows that all the combined studies and the overall effect are positive. Furthermore, it is simply understood from the forest graph in Fig.  3 and the effect size statistics in Table 3 that the results of the meta-analysis study conducted with 27 studies and analyzing the effect of online education on academic achievement illustrate that this relationship is on average level (= 0.409).

After the analysis of the effect size in the study, whether the studies included in the analysis are distributed heterogeneously or not has also been analyzed. The heterogeneity of the combined studies was determined through the Q and I 2 tests. As a result of the heterogeneity test, Q statistical value was calculated as 29.576. With 26 degrees of freedom at 95% significance level in the chi-square table, the critical value is accepted as 38.885. The Q statistical value (29.576) counted in this study is lower than the critical value of 38.885. The I 2 value, which is the complementary of the Q statistics, is 12.100%. This value indicates that the accurate heterogeneity or the total variability that can be attributed to variability between the studies is 12%. Besides, p value is higher than (0.285) p = 0.05. All these values [Q (26) = 29.579, p = 0.285; I2 = 12.100] indicate that there is a homogeneous distribution between the effect sizes, and fixed effects model should be used to interpret these effect sizes. However, some researchers argue that even if the heterogeneity is low, it should be evaluated based on the random effects model (Borenstein et al., 2007 ). Therefore, this study gives information about both models. The heterogeneity of the combined studies has been attempted to be explained with the characteristics of the studies included in the analysis. In this context, the final purpose of the study is to determine the effect of the country, academic level, and year variables on the findings. Accordingly, the statistics regarding the comparison of the stated relations according to the countries where the studies were conducted are given in Table 4 .

As seen in Table 4 , the effect of online education on academic achievement does not differ significantly according to the countries where the studies were conducted in. Q test results indicate the heterogeneity of the relationships between the variables in terms of countries where the studies were conducted in. According to the table, the effect of online education on academic achievement was reported as the highest in other countries, and the lowest in the US. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 5 .

As seen in Table 5 , the effect of online education on academic achievement does not differ according to the class level. However, the effect of online education on academic achievement is the highest in the 4 th class. The statistics regarding the comparison of the stated relations according to the class levels are given in Table 6 .

As seen in Table 6 , the effect of online education on academic achievement does not differ according to the school subjects included in the studies. However, the effect of online education on academic achievement is the highest in ICT subject.

The obtained effect size in the study was formed as a result of the findings attained from primary studies conducted in 7 different countries. In addition, these studies are the ones on different approaches to online education (online learning environments, social networks, blended learning, etc.). In this respect, the results may raise some questions about the validity and generalizability of the results of the study. However, the moderator analyzes, whether for the country variable or for the approaches covered by online education, did not create significant differences in terms of the effect sizes. If significant differences were to occur in terms of effect sizes, we could say that the comparisons we will make by comparing countries under the umbrella of online education would raise doubts in terms of generalizability. Moreover, no study has been found in the literature that is not based on a special approach or does not contain a specific technique conducted under the name of online education alone. For instance, one of the commonly used definitions is blended education which is defined as an educational model in which online education is combined with traditional education method (Colis & Moonen, 2001 ). Similarly, Rasmussen ( 2003 ) defines blended learning as “a distance education method that combines technology (high technology such as television, internet, or low technology such as voice e-mail, conferences) with traditional education and training.” Further, Kerres and Witt (2003) define blended learning as “combining face-to-face learning with technology-assisted learning.” As it is clearly observed, online education, which has a wider scope, includes many approaches.

As seen in Table 7 , the effect of online education on academic achievement does not differ according to online education approaches included in the studies. However, the effect of online education on academic achievement is the highest in Web Based Problem Solving Approach.

4 Conclusions and discussion

Considering the developments during the pandemics, it is thought that the diversity in online education applications as an interdisciplinary pragmatist field will increase, and the learning content and processes will be enriched with the integration of new technologies into online education processes. Another prediction is that more flexible and accessible learning opportunities will be created in online education processes, and in this way, lifelong learning processes will be strengthened. As a result, it is predicted that in the near future, online education and even digital learning with a newer name will turn into the main ground of education instead of being an alternative or having a support function in face-to-face learning. The lessons learned from the early period online learning experience, which was passed with rapid adaptation due to the Covid19 epidemic, will serve to develop this method all over the world, and in the near future, online learning will become the main learning structure through increasing its functionality with the contribution of new technologies and systems. If we look at it from this point of view, there is a necessity to strengthen online education.

In this study, the effect of online learning on academic achievement is at a moderate level. To increase this effect, the implementation of online learning requires support from teachers to prepare learning materials, to design learning appropriately, and to utilize various digital-based media such as websites, software technology and various other tools to support the effectiveness of online learning (Rolisca & Achadiyah, 2014 ). According to research conducted by Rahayu et al. ( 2017 ), it has been proven that the use of various types of software increases the effectiveness and quality of online learning. Implementation of online learning can affect students' ability to adapt to technological developments in that it makes students use various learning resources on the internet to access various types of information, and enables them to get used to performing inquiry learning and active learning (Hart et al., 2019 ; Prestiadi et al., 2019 ). In addition, there may be many reasons for the low level of effect in this study. The moderator variables examined in this study could be a guide in increasing the level of practical effect. However, the effect size did not differ significantly for all moderator variables. Different moderator analyzes can be evaluated in order to increase the level of impact of online education on academic success. If confounding variables that significantly change the effect level are detected, it can be spoken more precisely in order to increase this level. In addition to the technical and financial problems, the level of impact will increase if a few other difficulties are eliminated such as students, lack of interaction with the instructor, response time, and lack of traditional classroom socialization.

In addition, COVID-19 pandemic related social distancing has posed extreme difficulties for all stakeholders to get online as they have to work in time constraints and resource constraints. Adopting the online learning environment is not just a technical issue, it is a pedagogical and instructive challenge as well. Therefore, extensive preparation of teaching materials, curriculum, and assessment is vital in online education. Technology is the delivery tool and requires close cross-collaboration between teaching, content and technology teams (CoSN, 2020 ).

Online education applications have been used for many years. However, it has come to the fore more during the pandemic process. This result of necessity has brought with it the discussion of using online education instead of traditional education methods in the future. However, with this research, it has been revealed that online education applications are moderately effective. The use of online education instead of face-to-face education applications can only be possible with an increase in the level of success. This may have been possible with the experience and knowledge gained during the pandemic process. Therefore, the meta-analysis of experimental studies conducted in the coming years will guide us. In this context, experimental studies using online education applications should be analyzed well. It would be useful to identify variables that can change the level of impacts with different moderators. Moderator analyzes are valuable in meta-analysis studies (for example, the role of moderators in Karl Pearson's typhoid vaccine studies). In this context, each analysis study sheds light on future studies. In meta-analyses to be made about online education, it would be beneficial to go beyond the moderators determined in this study. Thus, the contribution of similar studies to the field will increase more.

The purpose of this study is to determine the effect of online education on academic achievement. In line with this purpose, the studies that analyze the effect of online education approaches on academic achievement have been included in the meta-analysis. The total sample size of the studies included in the meta-analysis is 1772. While the studies included in the meta-analysis were conducted in the US, Taiwan, Turkey, China, Philippines, Ireland, and Georgia, the studies carried out in Europe could not be reached. The reason may be attributed to that there may be more use of quantitative research methods from a positivist perspective in the countries with an American academic tradition. As a result of the study, it was found out that the effect size of online education on academic achievement (g = 0.409) was moderate. In the studies included in the present research, we found that online education approaches were more effective than traditional ones. However, contrary to the present study, the analysis of comparisons between online and traditional education in some studies shows that face-to-face traditional learning is still considered effective compared to online learning (Ahmad et al., 2016 ; Hamdani & Priatna, 2020 ; Wei & Chou, 2020 ). Online education has advantages and disadvantages. The advantages of online learning compared to face-to-face learning in the classroom is the flexibility of learning time in online learning, the learning time does not include a single program, and it can be shaped according to circumstances (Lai et al., 2019 ). The next advantage is the ease of collecting assignments for students, as these can be done without having to talk to the teacher. Despite this, online education has several weaknesses, such as students having difficulty in understanding the material, teachers' inability to control students, and students’ still having difficulty interacting with teachers in case of internet network cuts (Swan, 2007 ). According to Astuti et al ( 2019 ), face-to-face education method is still considered better by students than e-learning because it is easier to understand the material and easier to interact with teachers. The results of the study illustrated that the effect size (g = 0.409) of online education on academic achievement is of medium level. Therefore, the results of the moderator analysis showed that the effect of online education on academic achievement does not differ in terms of country, lecture, class level, and online education approaches variables. After analyzing the literature, several meta-analyses on online education were published (Bernard et al., 2004 ; Machtmes & Asher, 2000 ; Zhao et al., 2005 ). Typically, these meta-analyzes also include the studies of older generation technologies such as audio, video, or satellite transmission. One of the most comprehensive studies on online education was conducted by Bernard et al. ( 2004 ). In this study, 699 independent effect sizes of 232 studies published from 1985 to 2001 were analyzed, and face-to-face education was compared to online education, with respect to success criteria and attitudes of various learners from young children to adults. In this meta-analysis, an overall effect size close to zero was found for the students' achievement (g +  = 0.01).

In another meta-analysis study carried out by Zhao et al. ( 2005 ), 98 effect sizes were examined, including 51 studies on online education conducted between 1996 and 2002. According to the study of Bernard et al. ( 2004 ), this meta-analysis focuses on the activities done in online education lectures. As a result of the research, an overall effect size close to zero was found for online education utilizing more than one generation technology for students at different levels. However, the salient point of the meta-analysis study of Zhao et al. is that it takes the average of different types of results used in a study to calculate an overall effect size. This practice is problematic because the factors that develop one type of learner outcome (e.g. learner rehabilitation), particularly course characteristics and practices, may be quite different from those that develop another type of outcome (e.g. learner's achievement), and it may even cause damage to the latter outcome. While mixing the studies with different types of results, this implementation may obscure the relationship between practices and learning.

Some meta-analytical studies have focused on the effectiveness of the new generation distance learning courses accessed through the internet for specific student populations. For instance, Sitzmann and others (Sitzmann et al., 2006 ) reviewed 96 studies published from 1996 to 2005, comparing web-based education of job-related knowledge or skills with face-to-face one. The researchers found that web-based education in general was slightly more effective than face-to-face education, but it is insufficient in terms of applicability ("knowing how to apply"). In addition, Sitzmann et al. ( 2006 ) revealed that Internet-based education has a positive effect on theoretical knowledge in quasi-experimental studies; however, it positively affects face-to-face education in experimental studies performed by random assignment. This moderator analysis emphasizes the need to pay attention to the factors of designs of the studies included in the meta-analysis. The designs of the studies included in this meta-analysis study were ignored. This can be presented as a suggestion to the new studies that will be conducted.

Another meta-analysis study was conducted by Cavanaugh et al. ( 2004 ), in which they focused on online education. In this study on internet-based distance education programs for students under 12 years of age, the researchers combined 116 results from 14 studies published between 1999 and 2004 to calculate an overall effect that was not statistically different from zero. The moderator analysis carried out in this study showed that there was no significant factor affecting the students' success. This meta-analysis used multiple results of the same study, ignoring the fact that different results of the same student would not be independent from each other.

In conclusion, some meta-analytical studies analyzed the consequences of online education for a wide range of students (Bernard et al., 2004 ; Zhao et al., 2005 ), and the effect sizes were generally low in these studies. Furthermore, none of the large-scale meta-analyzes considered the moderators, database quality standards or class levels in the selection of the studies, while some of them just referred to the country and lecture moderators. Advances in internet-based learning tools, the pandemic process, and increasing popularity in different learning contexts have required a precise meta-analysis of students' learning outcomes through online learning. Previous meta-analysis studies were typically based on the studies, involving narrow range of confounding variables. In the present study, common but significant moderators such as class level and lectures during the pandemic process were discussed. For instance, the problems have been experienced especially in terms of eligibility of class levels in online education platforms during the pandemic process. It was found that there is a need to study and make suggestions on whether online education can meet the needs of teachers and students.

Besides, the main forms of online education in the past were to watch the open lectures of famous universities and educational videos of institutions. In addition, online education is mainly a classroom-based teaching implemented by teachers in their own schools during the pandemic period, which is an extension of the original school education. This meta-analysis study will stand as a source to compare the effect size of the online education forms of the past decade with what is done today, and what will be done in the future.

Lastly, the heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

*Studies included in meta-analysis

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Ulum, H. The effects of online education on academic success: A meta-analysis study. Educ Inf Technol 27 , 429–450 (2022). https://doi.org/10.1007/s10639-021-10740-8

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How Effective Is Online Learning? What the Research Does and Doesn’t Tell Us

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Editor’s Note: This is part of a series on the practical takeaways from research.

The times have dictated school closings and the rapid expansion of online education. Can online lessons replace in-school time?

Clearly online time cannot provide many of the informal social interactions students have at school, but how will online courses do in terms of moving student learning forward? Research to date gives us some clues and also points us to what we could be doing to support students who are most likely to struggle in the online setting.

The use of virtual courses among K-12 students has grown rapidly in recent years. Florida, for example, requires all high school students to take at least one online course. Online learning can take a number of different forms. Often people think of Massive Open Online Courses, or MOOCs, where thousands of students watch a video online and fill out questionnaires or take exams based on those lectures.

In the online setting, students may have more distractions and less oversight, which can reduce their motivation.

Most online courses, however, particularly those serving K-12 students, have a format much more similar to in-person courses. The teacher helps to run virtual discussion among the students, assigns homework, and follows up with individual students. Sometimes these courses are synchronous (teachers and students all meet at the same time) and sometimes they are asynchronous (non-concurrent). In both cases, the teacher is supposed to provide opportunities for students to engage thoughtfully with subject matter, and students, in most cases, are required to interact with each other virtually.

Coronavirus and Schools

Online courses provide opportunities for students. Students in a school that doesn’t offer statistics classes may be able to learn statistics with virtual lessons. If students fail algebra, they may be able to catch up during evenings or summer using online classes, and not disrupt their math trajectory at school. So, almost certainly, online classes sometimes benefit students.

In comparisons of online and in-person classes, however, online classes aren’t as effective as in-person classes for most students. Only a little research has assessed the effects of online lessons for elementary and high school students, and even less has used the “gold standard” method of comparing the results for students assigned randomly to online or in-person courses. Jessica Heppen and colleagues at the American Institutes for Research and the University of Chicago Consortium on School Research randomly assigned students who had failed second semester Algebra I to either face-to-face or online credit recovery courses over the summer. Students’ credit-recovery success rates and algebra test scores were lower in the online setting. Students assigned to the online option also rated their class as more difficult than did their peers assigned to the face-to-face option.

Most of the research on online courses for K-12 students has used large-scale administrative data, looking at otherwise similar students in the two settings. One of these studies, by June Ahn of New York University and Andrew McEachin of the RAND Corp., examined Ohio charter schools; I did another with colleagues looking at Florida public school coursework. Both studies found evidence that online coursetaking was less effective.

About this series

This essay is the fifth in a series that aims to put the pieces of research together so that education decisionmakers can evaluate which policies and practices to implement.

The conveners of this project—Susanna Loeb, the director of Brown University’s Annenberg Institute for School Reform, and Harvard education professor Heather Hill—have received grant support from the Annenberg Institute for this series.

To suggest other topics for this series or join in the conversation, use #EdResearchtoPractice on Twitter.

Read the full series here .

It is not surprising that in-person courses are, on average, more effective. Being in person with teachers and other students creates social pressures and benefits that can help motivate students to engage. Some students do as well in online courses as in in-person courses, some may actually do better, but, on average, students do worse in the online setting, and this is particularly true for students with weaker academic backgrounds.

Students who struggle in in-person classes are likely to struggle even more online. While the research on virtual schools in K-12 education doesn’t address these differences directly, a study of college students that I worked on with Stanford colleagues found very little difference in learning for high-performing students in the online and in-person settings. On the other hand, lower performing students performed meaningfully worse in online courses than in in-person courses.

But just because students who struggle in in-person classes are even more likely to struggle online doesn’t mean that’s inevitable. Online teachers will need to consider the needs of less-engaged students and work to engage them. Online courses might be made to work for these students on average, even if they have not in the past.

Just like in brick-and-mortar classrooms, online courses need a strong curriculum and strong pedagogical practices. Teachers need to understand what students know and what they don’t know, as well as how to help them learn new material. What is different in the online setting is that students may have more distractions and less oversight, which can reduce their motivation. The teacher will need to set norms for engagement—such as requiring students to regularly ask questions and respond to their peers—that are different than the norms in the in-person setting.

Online courses are generally not as effective as in-person classes, but they are certainly better than no classes. A substantial research base developed by Karl Alexander at Johns Hopkins University and many others shows that students, especially students with fewer resources at home, learn less when they are not in school. Right now, virtual courses are allowing students to access lessons and exercises and interact with teachers in ways that would have been impossible if an epidemic had closed schools even a decade or two earlier. So we may be skeptical of online learning, but it is also time to embrace and improve it.

A version of this article appeared in the April 01, 2020 edition of Education Week as How Effective Is Online Learning?

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The coronavirus pandemic has forced students and educators across all levels of education to rapidly adapt to online learning. The impact of this — and the developments required to make it work — could permanently change how education is delivered.

The COVID-19 pandemic has forced the world to engage in the ubiquitous use of virtual learning. And while online and distance learning has been used before to maintain continuity in education, such as in the aftermath of earthquakes 1 , the scale of the current crisis is unprecedented. Speculation has now also begun about what the lasting effects of this will be and what education may look like in the post-COVID era. For some, an immediate retreat to the traditions of the physical classroom is required. But for others, the forced shift to online education is a moment of change and a time to reimagine how education could be delivered 2 .

Looking back

Online education has traditionally been viewed as an alternative pathway, one that is particularly well suited to adult learners seeking higher education opportunities. However, the emergence of the COVID-19 pandemic has required educators and students across all levels of education to adapt quickly to virtual courses. (The term ‘emergency remote teaching’ was coined in the early stages of the pandemic to describe the temporary nature of this transition 3 .) In some cases, instruction shifted online, then returned to the physical classroom, and then shifted back online due to further surges in the rate of infection. In other cases, instruction was offered using a combination of remote delivery and face-to-face: that is, students can attend online or in person (referred to as the HyFlex model 4 ). In either case, instructors just had to figure out how to make it work, considering the affordances and constraints of the specific learning environment to create learning experiences that were feasible and effective.

The use of varied delivery modes does, in fact, have a long history in education. Mechanical (and then later electronic) teaching machines have provided individualized learning programmes since the 1950s and the work of B. F. Skinner 5 , who proposed using technology to walk individual learners through carefully designed sequences of instruction with immediate feedback indicating the accuracy of their response. Skinner’s notions formed the first formalized representations of programmed learning, or ‘designed’ learning experiences. Then, in the 1960s, Fred Keller developed a personalized system of instruction 6 , in which students first read assigned course materials on their own, followed by one-on-one assessment sessions with a tutor, gaining permission to move ahead only after demonstrating mastery of the instructional material. Occasional class meetings were held to discuss concepts, answer questions and provide opportunities for social interaction. A personalized system of instruction was designed on the premise that initial engagement with content could be done independently, then discussed and applied in the social context of a classroom.

These predecessors to contemporary online education leveraged key principles of instructional design — the systematic process of applying psychological principles of human learning to the creation of effective instructional solutions — to consider which methods (and their corresponding learning environments) would effectively engage students to attain the targeted learning outcomes. In other words, they considered what choices about the planning and implementation of the learning experience can lead to student success. Such early educational innovations laid the groundwork for contemporary virtual learning, which itself incorporates a variety of instructional approaches and combinations of delivery modes.

Online learning and the pandemic

Fast forward to 2020, and various further educational innovations have occurred to make the universal adoption of remote learning a possibility. One key challenge is access. Here, extensive problems remain, including the lack of Internet connectivity in some locations, especially rural ones, and the competing needs among family members for the use of home technology. However, creative solutions have emerged to provide students and families with the facilities and resources needed to engage in and successfully complete coursework 7 . For example, school buses have been used to provide mobile hotspots, and class packets have been sent by mail and instructional presentations aired on local public broadcasting stations. The year 2020 has also seen increased availability and adoption of electronic resources and activities that can now be integrated into online learning experiences. Synchronous online conferencing systems, such as Zoom and Google Meet, have allowed experts from anywhere in the world to join online classrooms 8 and have allowed presentations to be recorded for individual learners to watch at a time most convenient for them. Furthermore, the importance of hands-on, experiential learning has led to innovations such as virtual field trips and virtual labs 9 . A capacity to serve learners of all ages has thus now been effectively established, and the next generation of online education can move from an enterprise that largely serves adult learners and higher education to one that increasingly serves younger learners, in primary and secondary education and from ages 5 to 18.

The COVID-19 pandemic is also likely to have a lasting effect on lesson design. The constraints of the pandemic provided an opportunity for educators to consider new strategies to teach targeted concepts. Though rethinking of instructional approaches was forced and hurried, the experience has served as a rare chance to reconsider strategies that best facilitate learning within the affordances and constraints of the online context. In particular, greater variance in teaching and learning activities will continue to question the importance of ‘seat time’ as the standard on which educational credits are based 10 — lengthy Zoom sessions are seldom instructionally necessary and are not aligned with the psychological principles of how humans learn. Interaction is important for learning but forced interactions among students for the sake of interaction is neither motivating nor beneficial.

While the blurring of the lines between traditional and distance education has been noted for several decades 11 , the pandemic has quickly advanced the erasure of these boundaries. Less single mode, more multi-mode (and thus more educator choices) is becoming the norm due to enhanced infrastructure and developed skill sets that allow people to move across different delivery systems 12 . The well-established best practices of hybrid or blended teaching and learning 13 have served as a guide for new combinations of instructional delivery that have developed in response to the shift to virtual learning. The use of multiple delivery modes is likely to remain, and will be a feature employed with learners of all ages 14 , 15 . Future iterations of online education will no longer be bound to the traditions of single teaching modes, as educators can support pedagogical approaches from a menu of instructional delivery options, a mix that has been supported by previous generations of online educators 16 .

Also significant are the changes to how learning outcomes are determined in online settings. Many educators have altered the ways in which student achievement is measured, eliminating assignments and changing assessment strategies altogether 17 . Such alterations include determining learning through strategies that leverage the online delivery mode, such as interactive discussions, student-led teaching and the use of games to increase motivation and attention. Specific changes that are likely to continue include flexible or extended deadlines for assignment completion 18 , more student choice regarding measures of learning, and more authentic experiences that involve the meaningful application of newly learned skills and knowledge 19 , for example, team-based projects that involve multiple creative and social media tools in support of collaborative problem solving.

In response to the COVID-19 pandemic, technological and administrative systems for implementing online learning, and the infrastructure that supports its access and delivery, had to adapt quickly. While access remains a significant issue for many, extensive resources have been allocated and processes developed to connect learners with course activities and materials, to facilitate communication between instructors and students, and to manage the administration of online learning. Paths for greater access and opportunities to online education have now been forged, and there is a clear route for the next generation of adopters of online education.

Before the pandemic, the primary purpose of distance and online education was providing access to instruction for those otherwise unable to participate in a traditional, place-based academic programme. As its purpose has shifted to supporting continuity of instruction, its audience, as well as the wider learning ecosystem, has changed. It will be interesting to see which aspects of emergency remote teaching remain in the next generation of education, when the threat of COVID-19 is no longer a factor. But online education will undoubtedly find new audiences. And the flexibility and learning possibilities that have emerged from necessity are likely to shift the expectations of students and educators, diminishing further the line between classroom-based instruction and virtual learning.

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Lockee, B.B. Online education in the post-COVID era. Nat Electron 4 , 5–6 (2021). https://doi.org/10.1038/s41928-020-00534-0

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How does virtual learning impact students in higher education?

Subscribe to the brown center on education policy newsletter, stephanie riegg cellini stephanie riegg cellini nonresident senior fellow - governance studies , brown center on education policy.

August 13, 2021

In 2020, the pandemic pushed millions of college students around the world into virtual learning. As the new academic year begins, many colleges in the U.S. are poised to bring students back to campus, but a large amount of uncertainty remains. Some institutions will undoubtedly continue to offer online or hybrid classes, even as in-person instruction resumes. At the same time, low vaccination rates, new coronavirus variants, and travel restrictions for international students may mean a return to fully online instruction for some U.S. students and many more around the world.

Public attention has largely focused on the learning losses of K-12 students who shifted online during the pandemic. Yet, we may have reason to be concerned about postsecondary students too. What can we expect from the move to virtual learning? How does virtual learning impact student outcomes? And how does it compare to in-person instruction at the postsecondary level?

Several new papers shed light on these issues, building on previous work in higher education and assessing the efficacy of online education in new contexts. The results are generally consistent with past research: Online coursework generally yields worse student performance than in-person coursework. The negative effects of online course-taking are particularly pronounced for less-academically prepared students and for students pursuing bachelor’s degrees. New evidence from 2020 also suggests that the switch to online course-taking in the pandemic led to declines in course completion. However, a few new studies point to some positive effects of online learning, too. This post discusses this new evidence and its implications for the upcoming academic year.

Evaluating online instruction in higher education

A number of studies have assessed online versus in-person learning at the college level in recent years. A key concern in this literature is that students typically self-select into online or in-person programs or courses, confounding estimates of student outcomes. That is, differences in the characteristics of students themselves may drive differences in the outcome measures we observe that are unrelated to the mode of instruction. In addition, the content, instructor, assignments, and other course features might differ across online and in-person modes as well, which makes apples-to-apples comparisons difficult.

The most compelling studies of online education draw on a random assignment design (i.e., randomized control trial or RCT) to isolate the causal effect of online versus in-person learning. Several pathbreaking studies were able to estimate causal impacts of performance on final exams or course grades in recent years. Virtually all of these studies found that online instruction resulted in lower student performance relative to in-person instruction; although in one case , students with hybrid instruction performed similarly to their in-person peers. Negative effects of online course-taking were particularly pronounced for males and less-academically prepared students.

A new paper by Kofoed and co-authors adds to this literature looking specifically at online learning during the COVID-19 pandemic in a novel context: the U.S. Military Academy at West Point. When many colleges moved classes completely online or let students choose their own mode of instruction at the start of the pandemic, West Point economics professors arranged to randomly assign students to in-person or online modes of learning. The same instructors taught one online and one in-person economics class each, and all materials, exams, and assignments were otherwise identical, minimizing biases that otherwise stand in the way of true comparisons. They find that online education lowered a student’s final grade by about 0.2 standard deviations. Their work also confirms the results of previous papers, finding that the negative effect of online learning was driven by students with lower academic ability. A follow-up survey of students’ experiences suggests that online students had trouble concentrating on their coursework and felt less connected to both their peers and instructors relative to their in-person peers.

Cacault et al. (2021) also use an RCT to assess the effects of online lectures in a Swiss university. The authors find that having access to a live-streamed lecture in addition to an in-person option improves the achievement of high-ability students, but lowers the achievement of low-ability students. The key to understanding this two-pronged effect is the counterfactual: When streamed lectures substitute for no attendance (e.g., if a student is ill), they can help students, but when streaming lectures substitute for in-person attendance, they can hurt students.

Broader impacts of online learning

One drawback of RCTs is that these studies are typically limited to a single college and often a single course within that college, so it is not clear if the results generalize to other contexts. Several papers in the literature draw on larger samples of students in non-randomized settings and mitigate selection problems with various econometric methods. These papers find common themes: Students in online courses generally get lower grades, are less likely to perform well in follow-on coursework, and are less likely to graduate than similar students taking in-person classes.

In a recent paper , my co-author Hernando Grueso and I add to this strand of the literature, expanding it to a very different context. We draw on data from the country of Colombia, where students take a mandatory exit exam when they graduate. Using these data, we can assess test scores as an outcome, rather than (more subjective) course grades used in other studies. We can also assess performance across a wide range of institutions, degree programs, and majors.

We find that bachelor’s degree students in online programs perform worse on nearly all test score measures—including math, reading, writing, and English—relative to their counterparts in similar on-campus programs. Results for shorter technical certificates, however, are more mixed. While online students perform significantly worse than on-campus students on exit exams in private institutions, they perform better in SENA, the main public vocational institution in the country, suggesting substantial heterogeneity across institutions in the quality of online programming. Interviews with SENA staff indicate that SENA’s approach of synchronous learning and real-world projects may be working for some online students, but we cannot definitively call this causal evidence, particularly because we can only observe the students who graduate.

A new working paper by Fischer et al. pushes beyond near-term outcomes, like grades and scores, to consider longer-term outcomes, like graduation and time-to-degree, for bachelor’s degree-seeking students in a large public university in California. They find reason to be optimistic about online coursework: When students take courses required for their major online, they are more likely to graduate in four years and see a small decrease in time-to-degree relative to students taking the requirements in-person.

On the other hand, new work considering course completion during the pandemic is less promising. Looking at student outcomes in spring 2020 in Virginia’s community college system, Bird et al. find that the switch to online instruction resulted in an 8.5% reduction in course completion. They find that both withdrawals and failures rose. They also confirm findings in the literature that negative impacts are more extreme among less-academically-prepared students.

Online learning in the fall and beyond

Much more research on virtual learning will undoubtedly be forthcoming post-pandemic. For now, college professors and administrators should consider that college students pushed online may be less prepared for future follow-on classes, their GPAs may be lower, course completion may suffer, and overall learning may have declined relative to in-person cohorts in previous years. These results seem particularly problematic for students with less academic preparation and those in bachelor’s degree programs.

The research is less clear on the impact of virtual instruction on college completion. Although course completion rates appear to be lower for online courses relative to in-person, the evidence is mixed on the impact of virtual instruction on graduation and time-to-degree. The negative learning impacts, reduced course completion, and lack of connection with other students and faculty in a virtual environment could ultimately reduce college completion rates. On the other hand, there is also evidence that the availability of online classes may allow students to move through their degree requirement more quickly.

As the fall semester approaches, colleges will need to make critical choices about online, hybrid, and in-person course offerings. Maintaining some of the most successful online courses will enhance flexibility at this uncertain time and allow some students to continue to make progress on their degrees if they get sick or cannot return to campus for other reasons. For those transitioning back to campus, administrators might consider additional in-person programming, review sessions, tutoring, and other enhanced supports as students make up for learning losses associated with the virtual instruction of the past year.

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Online learning’s impact on student performance

Alex rees-jones of the wharton school co-authored a study that found that online learning during the pandemic had a negative impact on student learning..

A study published in Economics Letters co-authored by Alex Rees-Jones , an associate professor of business economics and public policy at the Wharton School, and led by Douglas M. McKee and George Orlov of Cornell University, found that online learning during the COVID-19 pandemic hurt student learning, but did not hurt particular demographic groups more than others. However, they did find that if the instructor used active learning techniques, students were more engaged and thus learning outcomes improved.

In this project, the researchers were studying the impact of the switch to online teaching on student performance during the beginning of the pandemic in spring 2020.

“I was one of a group of professors who were part of a multi-year program meant to assess and improve active learning techniques in the classroom,” says Rees-Jones. “We were running standardized tests at the end of each semester so we could see the effect of changes. By chance, COVID happened during all of this, so the cross-semester system we built to measure changes in student learning could be used to assess what happened from COVID.”

The bottom line to the study, according to Rees-Jones, is that the pandemic hurt student learning quite a bit.

“We studied if it hurt particular demographic groups more than others, but found no evidence that this mattered in our context,” he says. “One thing that did matter, though, was the instructor’s use of active learning techniques. Using approaches built to improve student engagement mitigated a lot of the negative effects, and not using any of those techniques was associated with quite bad outcomes.”

The researchers compared student performance on standard assessments in spring 2020 to student performance in the same courses in either fall or spring 2019 to estimate the impact of the emergency switch to remote instruction induced by the COVID-19 pandemic. Using these data, they addressed three questions:

First, they examined how end-of-semester knowledge was influenced by the measures taken in spring 2020.

The typical difficulty in assessing a question like this is finding measures of learning that are comparable over time, according to Rees-Jones.

“For example, if the average grade on the final exam in one semester is an A and the average another semester is a B, you don’t actually know if the amount learned across semesters was different,” he says. “One final could simply have harder questions.”

To get around this issue, professors teaching these classes all made clear lists of topics that should be learned in their class, and designed a standardized assessment of knowledge of those topics that could be given at the end of the semester year after year.

“Comparing performance on this test across semesters then allows you to infer differences in how well the students came to master the key topic areas laid out for the course,” says Rees-Jones. “Using this measure, we found that end-of-the-semester test scores declined by .2 standard deviations during spring 2019, which quantitatively is a pretty substantial decline.”

Second, they assessed whether certain groups of students were more affected by the pandemic.

“Specifically, we predicted student’s end-of-semester performance using information on whether they identified as an underrepresented minority, a female, a first-generation college attendee, or someone speaking English as a second language,” says Orlov. “While we did find evidence of some differences in performance across these groups, we did not find evidence that these differences changed during spring 2020.”

This suggests that, at least in the classes that were studied, according to Orlov, the negative effects of the pandemic were not particularly concentrated in one of these demographic groups.

“It would of course have very worrying equity implications if such differences were found,” he says.

And third, the study looked at whether the use of specific teaching methods resulted in a more successful transition to remote teaching. Earlier research has shown repeatedly that students learn more when they actively work on problems either individually or together in the classroom relative to students who sit passively listening to a lecture and taking notes, according to McKee.

“We thought going into this project that these teaching methods could work especially well in this online-during-a-pandemic setting where students are more easily distracted and are hungry for social interaction,” he says. “So we were not surprised to find that students in classes with planned peer interaction scored significantly higher on our assessments.”

The study’s findings make the authors optimistic about future student learning outcomes even though many students and teachers remain in a period of substantial online instruction for three reasons, according to the authors of the study.

“First, online teaching experience seems to matter, and during 2020, many college faculty accumulated some experience,” says Rees-Jones. “Second, we expected that disadvantaged groups would be further disadvantaged during the pandemic, but we found no statistical evidence of this concern,” says Orlov.

“Third, we have shown that it is possible to incorporate peer interaction or small group activities into synchronous online courses, and that it was significantly associated with improved learning, especially during the remotely taught portion of the semester,” says McKee.

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Research Article

COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis

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

¶ ‡ JZ and YD are contributed equally to this work as first authors.

Affiliation School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China

Roles Data curation, Formal analysis, Methodology, Writing – original draft

Affiliations School of Educational Information Technology, South China Normal University, Guangzhou, Guangdong, China, Hangzhou Zhongce Vocational School Qiantang, Hangzhou, Zhejiang, China

Roles Data curation, Writing – original draft

Roles Data curation

Roles Writing – original draft

Affiliation Faculty of Education, Shenzhen University, Shenzhen, Guangdong, China

Roles Conceptualization, Supervision, Writing – review & editing

* E-mail: [email protected] (JH); [email protected] (YZ)

• Junyi Zhang, 
• Yigang Ding, 
• Xinru Yang, 
• Jinping Zhong, 
• XinXin Qiu, 
• Zhishan Zou, 
• Yujie Xu, 
• Xiunan Jin, 
• Xiaomin Wu, 

• Published: August 23, 2022
• https://doi.org/10.1371/journal.pone.0273016
• Reader Comments

The COVID-19 outbreak brought online learning to the forefront of education. Scholars have conducted many studies on online learning during the pandemic, but only a few have performed quantitative comparative analyses of students’ online learning behavior before and after the outbreak. We collected review data from China’s massive open online course platform called icourse.163 and performed social network analysis on 15 courses to explore courses’ interaction characteristics before, during, and after the COVID-19 pan-demic. Specifically, we focused on the following aspects: (1) variations in the scale of online learning amid COVID-19; (2a) the characteristics of online learning interaction during the pandemic; (2b) the characteristics of online learning interaction after the pandemic; and (3) differences in the interaction characteristics of social science courses and natural science courses. Results revealed that only a small number of courses witnessed an uptick in online interaction, suggesting that the pandemic’s role in promoting the scale of courses was not significant. During the pandemic, online learning interaction became more frequent among course network members whose interaction scale increased. After the pandemic, although the scale of interaction declined, online learning interaction became more effective. The scale and level of interaction in Electrodynamics (a natural science course) and Economics (a social science course) both rose during the pan-demic. However, long after the pandemic, the Economics course sustained online interaction whereas interaction in the Electrodynamics course steadily declined. This discrepancy could be due to the unique characteristics of natural science courses and social science courses.

Citation: Zhang J, Ding Y, Yang X, Zhong J, Qiu X, Zou Z, et al. (2022) COVID-19’s impacts on the scope, effectiveness, and interaction characteristics of online learning: A social network analysis. PLoS ONE 17(8): e0273016. https://doi.org/10.1371/journal.pone.0273016

Editor: Heng Luo, Central China Normal University, CHINA

Received: April 20, 2022; Accepted: July 29, 2022; Published: August 23, 2022

Copyright: © 2022 Zhang 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: The data underlying the results presented in the study were downloaded from https://www.icourse163.org/ and are now shared fully on Github ( https://github.com/zjyzhangjunyi/dataset-from-icourse163-for-SNA ). These data have no private information and can be used for academic research free of charge.

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

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

1. Introduction

The development of the mobile internet has spurred rapid advances in online learning, offering novel prospects for teaching and learning and a learning experience completely different from traditional instruction. Online learning harnesses the advantages of network technology and multimedia technology to transcend the boundaries of conventional education [ 1 ]. Online courses have become a popular learning mode owing to their flexibility and openness. During online learning, teachers and students are in different physical locations but interact in multiple ways (e.g., via online forum discussions and asynchronous group discussions). An analysis of online learning therefore calls for attention to students’ participation. Alqurashi [ 2 ] defined interaction in online learning as the process of constructing meaningful information and thought exchanges between more than two people; such interaction typically occurs between teachers and learners, learners and learners, and the course content and learners.

Massive open online courses (MOOCs), a 21st-century teaching mode, have greatly influenced global education. Data released by China’s Ministry of Education in 2020 show that the country ranks first globally in the number and scale of higher education MOOCs. The COVID-19 outbreak has further propelled this learning mode, with universities being urged to leverage MOOCs and other online resource platforms to respond to government’s “School’s Out, But Class’s On” policy [ 3 ]. Besides MOOCs, to reduce in-person gatherings and curb the spread of COVID-19, various online learning methods have since become ubiquitous [ 4 ]. Though Lederman asserted that the COVID-19 outbreak has positioned online learning technologies as the best way for teachers and students to obtain satisfactory learning experiences [ 5 ], it remains unclear whether the COVID-19 pandemic has encouraged interaction in online learning, as interactions between students and others play key roles in academic performance and largely determine the quality of learning experiences [ 6 ]. Similarly, it is also unclear what impact the COVID-19 pandemic has had on the scale of online learning.

Social constructivism paints learning as a social phenomenon. As such, analyzing the social structures or patterns that emerge during the learning process can shed light on learning-based interaction [ 7 ]. Social network analysis helps to explain how a social network, rooted in interactions between learners and their peers, guides individuals’ behavior, emotions, and outcomes. This analytical approach is especially useful for evaluating interactive relationships between network members [ 8 ]. Mohammed cited social network analysis (SNA) as a method that can provide timely information about students, learning communities and interactive networks. SNA has been applied in numerous fields, including education, to identify the number and characteristics of interelement relationships. For example, Lee et al. also used SNA to explore the effects of blogs on peer relationships [ 7 ]. Therefore, adopting SNA to examine interactions in online learning communities during the COVID-19 pandemic can uncover potential issues with this online learning model.

Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, focusing on learners’ interaction characteristics before, during, and after the COVID-19 outbreak. We visually assessed changes in the scale of network interaction before, during, and after the outbreak along with the characteristics of interaction in Gephi. Examining students’ interactions in different courses revealed distinct interactive network characteristics, the pandemic’s impact on online courses, and relevant suggestions. Findings are expected to promote effective interaction and deep learning among students in addition to serving as a reference for the development of other online learning communities.

2. Literature review and research questions

Interaction is deemed as central to the educational experience and is a major focus of research on online learning. Moore began to study the problem of interaction in distance education as early as 1989. He defined three core types of interaction: student–teacher, student–content, and student–student [ 9 ]. Lear et al. [ 10 ] described an interactivity/ community-process model of distance education: they specifically discussed the relationships between interactivity, community awareness, and engaging learners and found interactivity and community awareness to be correlated with learner engagement. Zulfikar et al. [ 11 ] suggested that discussions initiated by the students encourage more students’ engagement than discussions initiated by the instructors. It is most important to afford learners opportunities to interact purposefully with teachers, and improving the quality of learner interaction is crucial to fostering profound learning [ 12 ]. Interaction is an important way for learners to communicate and share information, and a key factor in the quality of online learning [ 13 ].

Timely feedback is the main component of online learning interaction. Woo and Reeves discovered that students often become frustrated when they fail to receive prompt feedback [ 14 ]. Shelley et al. conducted a three-year study of graduate and undergraduate students’ satisfaction with online learning at universities and found that interaction with educators and students is the main factor affecting satisfaction [ 15 ]. Teachers therefore need to provide students with scoring justification, support, and constructive criticism during online learning. Some researchers examined online learning during the COVID-19 pandemic. They found that most students preferred face-to-face learning rather than online learning due to obstacles faced online, such as a lack of motivation, limited teacher-student interaction, and a sense of isolation when learning in different times and spaces [ 16 , 17 ]. However, it can be reduced by enhancing the online interaction between teachers and students [ 18 ].

Research showed that interactions contributed to maintaining students’ motivation to continue learning [ 19 ]. Baber argued that interaction played a key role in students’ academic performance and influenced the quality of the online learning experience [ 20 ]. Hodges et al. maintained that well-designed online instruction can lead to unique teaching experiences [ 21 ]. Banna et al. mentioned that using discussion boards, chat sessions, blogs, wikis, and other tools could promote student interaction and improve participation in online courses [ 22 ]. During the COVID-19 pandemic, Mahmood proposed a series of teaching strategies suitable for distance learning to improve its effectiveness [ 23 ]. Lapitan et al. devised an online strategy to ease the transition from traditional face-to-face instruction to online learning [ 24 ]. The preceding discussion suggests that online learning goes beyond simply providing learning resources; teachers should ideally design real-life activities to give learners more opportunities to participate.

As mentioned, COVID-19 has driven many scholars to explore the online learning environment. However, most have ignored the uniqueness of online learning during this time and have rarely compared pre- and post-pandemic online learning interaction. Taking China’s icourse.163 MOOC platform as an example, we chose 15 courses with a large number of participants for SNA, centering on student interaction before and after the pandemic. Gephi was used to visually analyze changes in the scale and characteristics of network interaction. The following questions were of particular interest:

• (1) Can the COVID-19 pandemic promote the expansion of online learning?
• (2a) What are the characteristics of online learning interaction during the pandemic?
• (2b) What are the characteristics of online learning interaction after the pandemic?
• (3) How do interaction characteristics differ between social science courses and natural science courses?

3. Methodology

3.1 research context.

We selected several courses with a large number of participants and extensive online interaction among hundreds of courses on the icourse.163 MOOC platform. These courses had been offered on the platform for at least three semesters, covering three periods (i.e., before, during, and after the COVID-19 outbreak). To eliminate the effects of shifts in irrelevant variables (e.g., course teaching activities), we chose several courses with similar teaching activities and compared them on multiple dimensions. All course content was taught online. The teachers of each course posted discussion threads related to learning topics; students were expected to reply via comments. Learners could exchange ideas freely in their responses in addition to asking questions and sharing their learning experiences. Teachers could answer students’ questions as well. Conversations in the comment area could partly compensate for a relative absence of online classroom interaction. Teacher–student interaction is conducive to the formation of a social network structure and enabled us to examine teachers’ and students’ learning behavior through SNA. The comment areas in these courses were intended for learners to construct knowledge via reciprocal communication. Meanwhile, by answering students’ questions, teachers could encourage them to reflect on their learning progress. These courses’ successive terms also spanned several phases of COVID-19, allowing us to ascertain the pandemic’s impact on online learning.

3.2 Data collection and preprocessing

To avoid interference from invalid or unclear data, the following criteria were applied to select representative courses: (1) generality (i.e., public courses and professional courses were chosen from different schools across China); (2) time validity (i.e., courses were held before during, and after the pandemic); and (3) notability (i.e., each course had at least 2,000 participants). We ultimately chose 15 courses across the social sciences and natural sciences (see Table 1 ). The coding is used to represent the course name.

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https://doi.org/10.1371/journal.pone.0273016.t001

To discern courses’ evolution during the pandemic, we gathered data on three terms before, during, and after the COVID-19 outbreak in addition to obtaining data from two terms completed well before the pandemic and long after. Our final dataset comprised five sets of interactive data. Finally, we collected about 120,000 comments for SNA. Because each course had a different start time—in line with fluctuations in the number of confirmed COVID-19 cases in China and the opening dates of most colleges and universities—we divided our sample into five phases: well before the pandemic (Phase I); before the pandemic (Phase Ⅱ); during the pandemic (Phase Ⅲ); after the pandemic (Phase Ⅳ); and long after the pandemic (Phase Ⅴ). We sought to preserve consistent time spans to balance the amount of data in each period ( Fig 1 ).

https://doi.org/10.1371/journal.pone.0273016.g001

3.3 Instrumentation

Participants’ comments and “thumbs-up” behavior data were converted into a network structure and compared using social network analysis (SNA). Network analysis, according to M’Chirgui, is an effective tool for clarifying network relationships by employing sophisticated techniques [ 25 ]. Specifically, SNA can help explain the underlying relationships among team members and provide a better understanding of their internal processes. Yang and Tang used SNA to discuss the relationship between team structure and team performance [ 26 ]. Golbeck argued that SNA could improve the understanding of students’ learning processes and reveal learners’ and teachers’ role dynamics [ 27 ].

To analyze Question (1), the number of nodes and diameter in the generated network were deemed as indicators of changes in network size. Social networks are typically represented as graphs with nodes and degrees, and node count indicates the sample size [ 15 ]. Wellman et al. proposed that the larger the network scale, the greater the number of network members providing emotional support, goods, services, and companionship [ 28 ]. Jan’s study measured the network size by counting the nodes which represented students, lecturers, and tutors [ 29 ]. Similarly, network nodes in the present study indicated how many learners and teachers participated in the course, with more nodes indicating more participants. Furthermore, we investigated the network diameter, a structural feature of social networks, which is a common metric for measuring network size in SNA [ 30 ]. The network diameter refers to the longest path between any two nodes in the network. There has been evidence that a larger network diameter leads to greater spread of behavior [ 31 ]. Likewise, Gašević et al. found that larger networks were more likely to spread innovative ideas about educational technology when analyzing MOOC-related research citations [ 32 ]. Therefore, we employed node count and network diameter to measure the network’s spatial size and further explore the expansion characteristic of online courses. Brief introduction of these indicators can be summarized in Table 2 .

https://doi.org/10.1371/journal.pone.0273016.t002

To address Question (2), a list of interactive analysis metrics in SNA were introduced to scrutinize learners’ interaction characteristics in online learning during and after the pandemic, as shown below:

• (1) The average degree reflects the density of the network by calculating the average number of connections for each node. As Rong and Xu suggested, the average degree of a network indicates how active its participants are [ 33 ]. According to Hu, a higher average degree implies that more students are interacting directly with each other in a learning context [ 34 ]. The present study inherited the concept of the average degree from these previous studies: the higher the average degree, the more frequent the interaction between individuals in the network.
• (2) Essentially, a weighted average degree in a network is calculated by multiplying each degree by its respective weight, and then taking the average. Bydžovská took the strength of the relationship into account when determining the weighted average degree [ 35 ]. By calculating friendship’s weighted value, Maroulis assessed peer achievement within a small-school reform [ 36 ]. Accordingly, we considered the number of interactions as the weight of the degree, with a higher average degree indicating more active interaction among learners.
• (3) Network density is the ratio between actual connections and potential connections in a network. The more connections group members have with each other, the higher the network density. In SNA, network density is similar to group cohesion, i.e., a network of more strong relationships is more cohesive [ 37 ]. Network density also reflects how much all members are connected together [ 38 ]. Therefore, we adopted network density to indicate the closeness among network members. Higher network density indicates more frequent interaction and closer communication among students.
• (4) Clustering coefficient describes local network attributes and indicates that two nodes in the network could be connected through adjacent nodes. The clustering coefficient measures users’ tendency to gather (cluster) with others in the network: the higher the clustering coefficient, the more frequently users communicate with other group members. We regarded this indicator as a reflection of the cohesiveness of the group [ 39 ].
• (5) In a network, the average path length is the average number of steps along the shortest paths between any two nodes. Oliveres has observed that when an average path length is small, the route from one node to another is shorter when graphed [ 40 ]. This is especially true in educational settings where students tend to become closer friends. So we consider that the smaller the average path length, the greater the possibility of interaction between individuals in the network.
• (6) A network with a large number of nodes, but whose average path length is surprisingly small, is known as the small-world effect [ 41 ]. A higher clustering coefficient and shorter average path length are important indicators of a small-world network: a shorter average path length enables the network to spread information faster and more accurately; a higher clustering coefficient can promote frequent knowledge exchange within the group while boosting the timeliness and accuracy of knowledge dissemination [ 42 ]. Brief introduction of these indicators can be summarized in Table 3 .

https://doi.org/10.1371/journal.pone.0273016.t003

To analyze Question 3, we used the concept of closeness centrality, which determines how close a vertex is to others in the network. As Opsahl et al. explained, closeness centrality reveals how closely actors are coupled with their entire social network [ 43 ]. In order to analyze social network-based engineering education, Putnik et al. examined closeness centrality and found that it was significantly correlated with grades [ 38 ]. We used closeness centrality to measure the position of an individual in the network. Brief introduction of these indicators can be summarized in Table 4 .

https://doi.org/10.1371/journal.pone.0273016.t004

3.4 Ethics statement

This study was approved by the Academic Committee Office (ACO) of South China Normal University ( http://fzghb.scnu.edu.cn/ ), Guangzhou, China. Research data were collected from the open platform and analyzed anonymously. There are thus no privacy issues involved in this study.

4.1 COVID-19’s role in promoting the scale of online courses was not as important as expected

As shown in Fig 2 , the number of course participants and nodes are closely correlated with the pandemic’s trajectory. Because the number of participants in each course varied widely, we normalized the number of participants and nodes to more conveniently visualize course trends. Fig 2 depicts changes in the chosen courses’ number of participants and nodes before the pandemic (Phase II), during the pandemic (Phase III), and after the pandemic (Phase IV). The number of participants in most courses during the pandemic exceeded those before and after the pandemic. But the number of people who participate in interaction in some courses did not increase.

https://doi.org/10.1371/journal.pone.0273016.g002

In order to better analyze the trend of interaction scale in online courses before, during, and after the pandemic, the selected courses were categorized according to their scale change. When the number of participants increased (decreased) beyond 20% (statistical experience) and the diameter also increased (decreased), the course scale was determined to have increased (decreased); otherwise, no significant change was identified in the course’s interaction scale. Courses were subsequently divided into three categories: increased interaction scale, decreased interaction scale, and no significant change. Results appear in Table 5 .

https://doi.org/10.1371/journal.pone.0273016.t005

From before the pandemic until it broke out, the interaction scale of five courses increased, accounting for 33.3% of the full sample; one course’s interaction scale declined, accounting for 6.7%. The interaction scale of nine courses decreased, accounting for 60%. The pandemic’s role in promoting online courses thus was not as important as anticipated, and most courses’ interaction scale did not change significantly throughout.

No courses displayed growing interaction scale after the pandemic: the interaction scale of nine courses fell, accounting for 60%; and the interaction scale of six courses did not shift significantly, accounting for 40%. Courses with an increased scale of interaction during the pandemic did not maintain an upward trend. On the contrary, the improvement in the pandemic caused learners’ enthusiasm for online learning to wane. We next analyzed several interaction metrics to further explore course interaction during different pandemic periods.

4.2 Characteristics of online learning interaction amid COVID-19

4.2.1 during the covid-19 pandemic, online learning interaction in some courses became more active..

Changes in course indicators with the growing interaction scale during the pandemic are presented in Fig 3 , including SS5, SS6, NS1, NS3, and NS8. The horizontal ordinate indicates the number of courses, with red color representing the rise of the indicator value on the vertical ordinate and blue representing the decline.

https://doi.org/10.1371/journal.pone.0273016.g003

Specifically: (1) The average degree and weighted average degree of the five course networks demonstrated an upward trend. The emergence of the pandemic promoted students’ enthusiasm; learners were more active in the interactive network. (2) Fig 3 shows that 3 courses had increased network density and 2 courses had decreased. The higher the network density, the more communication within the team. Even though the pandemic accelerated the interaction scale and frequency, the tightness between learners in some courses did not improve. (3) The clustering coefficient of social science courses rose whereas the clustering coefficient and small-world property of natural science courses fell. The higher the clustering coefficient and the small-world property, the better the relationship between adjacent nodes and the higher the cohesion [ 39 ]. (4) Most courses’ average path length increased as the interaction scale increased. However, when the average path length grew, adverse effects could manifest: communication between learners might be limited to a small group without multi-directional interaction.

When the pandemic emerged, the only declining network scale belonged to a natural science course (NS2). The change in each course index is pictured in Fig 4 . The abscissa indicates the size of the value, with larger values to the right. The red dot indicates the index value before the pandemic; the blue dot indicates its value during the pandemic. If the blue dot is to the right of the red dot, then the value of the index increased; otherwise, the index value declined. Only the weighted average degree of the course network increased. The average degree, network density decreased, indicating that network members were not active and that learners’ interaction degree and communication frequency lessened. Despite reduced learner interaction, the average path length was small and the connectivity between learners was adequate.

https://doi.org/10.1371/journal.pone.0273016.g004

4.2.2 After the COVID-19 pandemic, the scale decreased rapidly, but most course interaction was more effective.

Fig 5 shows the changes in various courses’ interaction indicators after the pandemic, including SS1, SS2, SS3, SS6, SS7, NS2, NS3, NS7, and NS8.

https://doi.org/10.1371/journal.pone.0273016.g005

Specifically: (1) The average degree and weighted average degree of most course networks decreased. The scope and intensity of interaction among network members declined rapidly, as did learners’ enthusiasm for communication. (2) The network density of seven courses also fell, indicating weaker connections between learners in most courses. (3) In addition, the clustering coefficient and small-world property of most course networks decreased, suggesting little possibility of small groups in the network. The scope of interaction between learners was not limited to a specific space, and the interaction objects had no significant tendencies. (4) Although the scale of course interaction became smaller in this phase, the average path length of members’ social networks shortened in nine courses. Its shorter average path length would expedite the spread of information within the network as well as communication and sharing among network members.

Fig 6 displays the evolution of course interaction indicators without significant changes in interaction scale after the pandemic, including SS4, SS5, NS1, NS4, NS5, and NS6.

https://doi.org/10.1371/journal.pone.0273016.g006

Specifically: (1) Some course members’ social networks exhibited an increase in the average and weighted average. In these cases, even though the course network’s scale did not continue to increase, communication among network members rose and interaction became more frequent and deeper than before. (2) Network density and average path length are indicators of social network density. The greater the network density, the denser the social network; the shorter the average path length, the more concentrated the communication among network members. However, at this phase, the average path length and network density in most courses had increased. Yet the network density remained small despite having risen ( Table 6 ). Even with more frequent learner interaction, connections remained distant and the social network was comparatively sparse.

https://doi.org/10.1371/journal.pone.0273016.t006

In summary, the scale of interaction did not change significantly overall. Nonetheless, some course members’ frequency and extent of interaction increased, and the relationships between network members became closer as well. In the study, we found it interesting that the interaction scale of Economics (a social science course) course and Electrodynamics (a natural science course) course expanded rapidly during the pandemic and retained their interaction scale thereafter. We next assessed these two courses to determine whether their level of interaction persisted after the pandemic.

4.3 Analyses of natural science courses and social science courses

4.3.1 analyses of the interaction characteristics of economics and electrodynamics..

Economics and Electrodynamics are social science courses and natural science courses, respectively. Members’ interaction within these courses was similar: the interaction scale increased significantly when COVID-19 broke out (Phase Ⅲ), and no significant changes emerged after the pandemic (Phase Ⅴ). We hence focused on course interaction long after the outbreak (Phase V) and compared changes across multiple indicators, as listed in Table 7 .

https://doi.org/10.1371/journal.pone.0273016.t007

As the pandemic continued to improve, the number of participants and the diameter long after the outbreak (Phase V) each declined for Economics compared with after the pandemic (Phase IV). The interaction scale decreased, but the interaction between learners was much deeper. Specifically: (1) The weighted average degree, network density, clustering coefficient, and small-world property each reflected upward trends. The pandemic therefore exerted a strong impact on this course. Interaction was well maintained even after the pandemic. The smaller network scale promoted members’ interaction and communication. (2) Compared with after the pandemic (Phase IV), members’ network density increased significantly, showing that relationships between learners were closer and that cohesion was improving. (3) At the same time, as the clustering coefficient and small-world property grew, network members demonstrated strong small-group characteristics: the communication between them was deepening and their enthusiasm for interaction was higher. (4) Long after the COVID-19 outbreak (Phase V), the average path length was reduced compared with previous terms, knowledge flowed more quickly among network members, and the degree of interaction gradually deepened.

The average degree, weighted average degree, network density, clustering coefficient, and small-world property of Electrodynamics all decreased long after the COVID-19 outbreak (Phase V) and were lower than during the outbreak (Phase Ⅲ). The level of learner interaction therefore gradually declined long after the outbreak (Phase V), and connections between learners were no longer active. Although the pandemic increased course members’ extent of interaction, this rise was merely temporary: students’ enthusiasm for learning waned rapidly and their interaction decreased after the pandemic (Phase IV). To further analyze the interaction characteristics of course members in Economics and Electrodynamics, we evaluated the closeness centrality of their social networks, as shown in section 4.3.2.

4.3.2 Analysis of the closeness centrality of Economics and Electrodynamics.

The change in the closeness centrality of social networks in Economics was small, and no sharp upward trend appeared during the pandemic outbreak, as shown in Fig 7 . The emergence of COVID-19 apparently fostered learners’ interaction in Economics albeit without a significant impact. The closeness centrality changed in Electrodynamics varied from that of Economics: upon the COVID-19 outbreak, closeness centrality was significantly different from other semesters. Communication between learners was closer and interaction was more effective. Electrodynamics course members’ social network proximity decreased rapidly after the pandemic. Learners’ communication lessened. In general, Economics course showed better interaction before the outbreak and was less affected by the pandemic; Electrodynamics course was more affected by the pandemic and showed different interaction characteristics at different periods of the pandemic.

(Note: "****" indicates the significant distinction in closeness centrality between the two periods, otherwise no significant distinction).

https://doi.org/10.1371/journal.pone.0273016.g007

5. Discussion

We referred to discussion forums from several courses on the icourse.163 MOOC platform to compare online learning before, during, and after the COVID-19 pandemic via SNA and to delineate the pandemic’s effects on online courses. Only 33.3% of courses in our sample increased in terms of interaction during the pandemic; the scale of interaction did not rise in any courses thereafter. When the courses scale rose, the scope and frequency of interaction showed upward trends during the pandemic; and the clustering coefficient of natural science courses and social science courses differed: the coefficient for social science courses tended to rise whereas that for natural science courses generally declined. When the pandemic broke out, the interaction scale of a single natural science course decreased along with its interaction scope and frequency. The amount of interaction in most courses shrank rapidly during the pandemic and network members were not as active as they had been before. However, after the pandemic, some courses saw declining interaction but greater communication between members; interaction also became more frequent and deeper than before.

5.1 During the COVID-19 pandemic, the scale of interaction increased in only a few courses

The pandemic outbreak led to a rapid increase in the number of participants in most courses; however, the change in network scale was not significant. The scale of online interaction expanded swiftly in only a few courses; in others, the scale either did not change significantly or displayed a downward trend. After the pandemic, the interaction scale in most courses decreased quickly; the same pattern applied to communication between network members. Learners’ enthusiasm for online interaction reduced as the circumstances of the pandemic improved—potentially because, during the pandemic, China’s Ministry of Education declared “School’s Out, But Class’s On” policy. Major colleges and universities were encouraged to use the Internet and informational resources to provide learning support, hence the sudden increase in the number of participants and interaction in online courses [ 46 ]. After the pandemic, students’ enthusiasm for online learning gradually weakened, presumably due to easing of the pandemic [ 47 ]. More activities also transitioned from online to offline, which tempered learners’ online discussion. Research has shown that long-term online learning can even bore students [ 48 ].

Most courses’ interaction scale decreased significantly after the pandemic. First, teachers and students occupied separate spaces during the outbreak, had few opportunities for mutual cooperation and friendship, and lacked a sense of belonging [ 49 ]. Students’ enthusiasm for learning dissipated over time [ 50 ]. Second, some teachers were especially concerned about adapting in-person instructional materials for digital platforms; their pedagogical methods were ineffective, and they did not provide learning activities germane to student interaction [ 51 ]. Third, although teachers and students in remote areas were actively engaged in online learning, some students could not continue to participate in distance learning due to inadequate technology later in the outbreak [ 52 ].

5.2 Characteristics of online learning interaction during and after the COVID-19 pandemic

5.2.1 during the covid-19 pandemic, online interaction in most courses did not change significantly..

The interaction scale of only a few courses increased during the pandemic. The interaction scope and frequency of these courses climbed as well. Yet even as the degree of network interaction rose, course network density did not expand in all cases. The pandemic sparked a surge in the number of online learners and a rapid increase in network scale, but students found it difficult to interact with all learners. Yau pointed out that a greater network scale did not enrich the range of interaction between individuals; rather, the number of individuals who could interact directly was limited [ 53 ]. The internet facilitates interpersonal communication. However, not everyone has the time or ability to establish close ties with others [ 54 ].

In addition, social science courses and natural science courses in our sample revealed disparate trends in this regard: the clustering coefficient of social science courses increased and that of natural science courses decreased. Social science courses usually employ learning approaches distinct from those in natural science courses [ 55 ]. Social science courses emphasize critical and innovative thinking along with personal expression [ 56 ]. Natural science courses focus on practical skills, methods, and principles [ 57 ]. Therefore, the content of social science courses can spur large-scale discussion among learners. Some course evaluations indicated that the course content design was suboptimal as well: teachers paid close attention to knowledge transmission and much less to piquing students’ interest in learning. In addition, the thread topics that teachers posted were scarcely diversified and teachers’ questions lacked openness. These attributes could not spark active discussion among learners.

5.2.2 Online learning interaction declined after the COVID-19 pandemic.

Most courses’ interaction scale and intensity decreased rapidly after the pandemic, but some did not change. Courses with a larger network scale did not continue to expand after the outbreak, and students’ enthusiasm for learning paled. The pandemic’s reduced severity also influenced the number of participants in online courses. Meanwhile, restored school order moved many learning activities from virtual to in-person spaces. Face-to-face learning has gradually replaced online learning, resulting in lower enrollment and less interaction in online courses. Prolonged online courses could have also led students to feel lonely and to lack a sense of belonging [ 58 ].

The scale of interaction in some courses did not change substantially after the pandemic yet learners’ connections became tighter. We hence recommend that teachers seize pandemic-related opportunities to design suitable activities. Additionally, instructors should promote student-teacher and student-student interaction, encourage students to actively participate online, and generally intensify the impact of online learning.

5.3 What are the characteristics of interaction in social science courses and natural science courses?

The level of interaction in Economics (a social science course) was significantly higher than that in Electrodynamics (a natural science course), and the small-world property in Economics increased as well. To boost online courses’ learning-related impacts, teachers can divide groups of learners based on the clustering coefficient and the average path length. Small groups of students may benefit teachers in several ways: to participate actively in activities intended to expand students’ knowledge, and to serve as key actors in these small groups. Cultivating students’ keenness to participate in class activities and self-management can also help teachers guide learner interaction and foster deep knowledge construction.

As evidenced by comments posted in the Electrodynamics course, we observed less interaction between students. Teachers also rarely urged students to contribute to conversations. These trends may have arisen because teachers and students were in different spaces. Teachers might have struggled to discern students’ interaction status. Teachers could also have failed to intervene in time, to design online learning activities that piqued learners’ interest, and to employ sound interactive theme planning and guidance. Teachers are often active in traditional classroom settings. Their roles are comparatively weakened online, such that they possess less control over instruction [ 59 ]. Online instruction also requires a stronger hand in learning: teachers should play a leading role in regulating network members’ interactive communication [ 60 ]. Teachers can guide learners to participate, help learners establish social networks, and heighten students’ interest in learning [ 61 ]. Teachers should attend to core members in online learning while also considering edge members; by doing so, all network members can be driven to share their knowledge and become more engaged. Finally, teachers and assistant teachers should help learners develop knowledge, exchange topic-related ideas, pose relevant questions during course discussions, and craft activities that enable learners to interact online [ 62 ]. These tactics can improve the effectiveness of online learning.

As described, network members displayed distinct interaction behavior in Economics and Electrodynamics courses. First, these courses varied in their difficulty: the social science course seemed easier to understand and focused on divergent thinking. Learners were often willing to express their views in comments and to ponder others’ perspectives [ 63 ]. The natural science course seemed more demanding and was oriented around logical thinking and skills [ 64 ]. Second, courses’ content differed. In general, social science courses favor the acquisition of declarative knowledge and creative knowledge compared with natural science courses. Social science courses also entertain open questions [ 65 ]. Natural science courses revolve around principle knowledge, strategic knowledge, and transfer knowledge [ 66 ]. Problems in these courses are normally more complicated than those in social science courses. Third, the indicators affecting students’ attitudes toward learning were unique. Guo et al. discovered that “teacher feedback” most strongly influenced students’ attitudes towards learning social science courses but had less impact on students in natural science courses [ 67 ]. Therefore, learners in social science courses likely expect more feedback from teachers and greater interaction with others.

6. Conclusion and future work

Our findings show that the network interaction scale of some online courses expanded during the COVID-19 pandemic. The network scale of most courses did not change significantly, demonstrating that the pandemic did not notably alter the scale of course interaction. Online learning interaction among course network members whose interaction scale increased also became more frequent during the pandemic. Once the outbreak was under control, although the scale of interaction declined, the level and scope of some courses’ interactive networks continued to rise; interaction was thus particularly effective in these cases. Overall, the pandemic appeared to have a relatively positive impact on online learning interaction. We considered a pair of courses in detail and found that Economics (a social science course) fared much better than Electrodynamics (a natural science course) in classroom interaction; learners were more willing to partake in-class activities, perhaps due to these courses’ unique characteristics. Brint et al. also came to similar conclusions [ 57 ].

This study was intended to be rigorous. Even so, several constraints can be addressed in future work. The first limitation involves our sample: we focused on a select set of courses hosted on China’s icourse.163 MOOC platform. Future studies should involve an expansive collection of courses to provide a more holistic understanding of how the pandemic has influenced online interaction. Second, we only explored the interactive relationship between learners and did not analyze interactive content. More in-depth content analysis should be carried out in subsequent research. All in all, the emergence of COVID-19 has provided a new path for online learning and has reshaped the distance learning landscape. To cope with associated challenges, educational practitioners will need to continue innovating in online instructional design, strengthen related pedagogy, optimize online learning conditions, and bolster teachers’ and students’ competence in online learning.

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Essays About Online Class: Top 5 Examples and 7 Prompts

Essays about online class tell many stories. If you need to write about e-learning, discover the top examples and prompts for the subject in our guide.

With over 5.8 million American students attending in 2021, online classes are now one of the education sector’s most popular and modern learning methods. Although it became prevalent because of the pandemic, it’s believed that the concept of distance learning began in the late 1800s .

Online classes pose many benefits that many still take advantage of even after the pandemic. However, not everyone adjusts well to this technology-centered learning due to no face-to-face contact and difficulty learning without the back-and-forth of lesson question time. 

1. My Experience as an Online Student by Debra Sicard

2. how to succeed in online classes essay by anonymous on ivypanda.com, 3. essay on advantages and disadvantages of online classes by anonymous on selfstudymantra.com, 4. online school vs. traditional school essay by anonymous on gradesfixer.com, 5. short essay on online classes by anonymous on byjus.com, 7 helpful prompts on essays about online class, 1. online classes: defined, 2. my experience with online classes during lockdown, 3. how does online class work, 4. the best sites for online class, 5. the pros and cons of enrolling in online class, 6. review of a book about online class, 7. should online classes be the norm.

“I am not a traditional student, so I have non-traditional needs… online classes fit my lifestyle.”

Sicard shares her positive experience with online classes, primarily centering her essay on convenience. She says that with online courses, she can fit more lessons into her schedule, save her money on gas, and have more time with her family. In addition, she mentions she can work and do other things besides taking her credits.

To have a proper perspective of the topic, Sicard also includes the disadvantages of virtual learning, such as devices catching viruses and missing in-real-life interactions with her professors and classmates. But, she believes that an online student can learn as much or even more than what students learn in traditional classes.

“In an online class, a student can only achieve success if he is committed to time management, balancing personal obligations, finding an ideal study environment, asking questions, and applying more effort to completing the course requirements.”

This essay contains steps a non-traditional student can take to avoid failing online classes. The author says that students, especially multitaskers, must know how to manage and balance their time to avoid losing focus. In addition, having a dedicated study spot is necessary to avoid distractions.

“Online classes or online method of learning presents an easy and comfortable method to achieve knowledge. Online classes have now become a great alternative to traditional classes.”

The writer delves into the benefits and drawbacks of online versus traditional learning. Virtual classes offer students freedom regarding their schedules and whereabouts. Some schools also allow students to learn for free. E-learning effectively trains individuals to be responsible and disciplined. 

However, individuals who are not computer literate will find online classes frustrating. Plus, electronic devices can be bad for health, and a lack of personal interaction can hinder personality development.

“[Online course] will also help you become more self-motivated, a trait that will make you stand out in the workplace and beyond.”

By listing the similarities and differences between online and traditional schools, the author demonstrates what classes a student should pick. The writer concludes that while traditional schools prepare students for the real world by interacting with diverse people, online schools help students become more self-motivated to stand out.

“The advantages of online classes take over their disadvantages. If students want to learn, then they have immense opportunities to learn from online classes.”

The author defines online classes as a type of education system where students use electronic devices with an internet connection to learn. However, while online learning improves the quality of education, it can also make the student lazy and cultivates a sense of isolation. Ultimately, they believe that to have the best education system, school teachers and officials must learn how to combine the two methods.

If the topic you’re thinking of is still confusing and you don’t know where to start, here are seven easy writing prompts to inspire you:

Explain the topic to your reader and give a brief history of the origins of online classes. Then briefly compare it to the traditional class to make the differences clear. Finally, point out the distinct features of online classes that conventional learning doesn’t offer, such as face-to-face interaction and question-and-answer debates. You can also discuss various online classes schools offer, such as hybrid learning, interactive online courses, etc.

Tell your story if you’re a student with experience with online classes. Narrate how your school switched to virtual classrooms. Relay the challenges you encountered, including how you adapted. Finish your essay by stating your current preference and why. 

For example, you favor e-learning because it cuts your transportation expenses, helps you be more responsible for managing your time, and lets you sleep in the mornings.

Relate your experience when your school moved online. Discuss any equipment or devices you need to buy before enrolling in your online class. Explain how your school handles online courses and what it does when there are technical difficulties. Add how these challenges (such as unstable internet connection and sudden power outage), such as attendance and participation, impact a class.

To make your essay more intriguing, add the average price of your online classes and if you think it’s fair. For instance, you can argue that since schools don’t provide computers and save expenses on cleaning and utilities when physical classrooms are unused, they should cut their laboratory or miscellaneous fees. You may also be interested in these articles about back to school .

Zoom, Google Classroom, and Microsoft Team are just three of the most popular online teaching software for online classes. In this prompt, look for the most useful and efficient software sites teachers or schools should incorporate into e-learning. Find examples or reliable data that show the number of students or schools that use them. Finally, ensure the details you add are accurate to make your essay credible.

Do you want to write about technology instead? Check out our  essays about technology .

Discussing online classes’ positive and negative effects is a usual essay topic. To make your essay stand out, pick the most impactful points on everyone involved. Don’t just explore the students’ perspectives. Include how virtual learning influences teachers, parents, and businesses.

To give you an idea, you can look into businesses near the campus that closed down when the school shifted to virtual classrooms.

This prompt requires you to search for publications about online classes and share your opinion on them.

For example, John F. Lyons’s book, How to Succeed in an Online Class , published in 2011, introduced technology students encounter in online classes. Suppose you read this book. First, enumerate Lyons’ advice, tips, and learning techniques to prevent a student from failing their online course. Then, briefly explain them individually and include examples or proof that his advice helped.

Online schooling has been around for a long time but has only become widespread because of the pandemic. Use this prompt to write your opinion on whether schools should make virtual learning a permanent option for students. Whatever your answer is, explain your reason to your readers.If you’re interested in learning more about essays, check out our essay writing tips !

Maria Caballero is a freelance writer who has been writing since high school. She believes that to be a writer doesn't only refer to excellent syntax and semantics but also knowing how to weave words together to communicate to any reader effectively.

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✍️Essay on Online Classes: Samples in 100, 150, 200 Words

• Updated on  
• Oct 20, 2023

Online classes, also known as virtual classes, have over time revolutionized education. They are known for providing students with the flexibility to access educational content and at the same time interact with professors in the comfort of their homes. With time, this mode of learning has gained huge popularity due to its accessibility and the ability to cater to diverse learning styles.

In this digital age, online classes have become a fundamental part of education, enabling all individuals to acquire knowledge, skills etc. Are you looking to gain some more information about online classes? Well, you have come to the right place. Here you will get to read some samples of online classes. 

Table of Contents

• 1 What are Online Classes?
• 2 Essay on Online Classes in 100 Words
• 3 Essay on Online Classes in 150 Words
• 4 Essay on Online Classes in 200 Words

Also Read: Online Courses

What are Online Classes?

Online classes are educational courses or learning programs which are conducted over the Internet. They provide students with the opportunity to study and complete their coursework remotely from the comfort of their homes. Online classes are a part of formal education. They can be taken in schools or colleges or can be offered by various online learning platforms. 

Online classes may include a variety of digital resources as well as tools. These may include quizzes, assignments, video lectures, discussion forums, connecting with friends via email, chat video calls etc. This type of learning offers the student flexibility in terms of when and where they can access their coursework and study. It is also helpful for those who study part-time have busy schedules and prefer remote learning. 

With the onset of COVID-19 , online classes became a huge hit hence the evolution of online classes. It offers one with different levels of education, skill training and much more. 

Essay on Online Classes in 100 Words

Online classes have become a central aspect of modern education. They offer flexibility, accessibility, and convenience, allowing students to learn from the comfort of their homes. The rise of online classes was accelerated during the COVID-19 pandemic, making a shift from traditional classrooms to virtual learning environments. 

However, there are many disadvantages to online classes. Students may struggle with distractions, lack of in-person interaction, and technical issues. Additionally, they have opened up new avenues for global collaboration and lifelong learning. In an increasingly digital world, online classes are likely to remain a significant part of education.

Essay on Online Classes in 150 Words

Online classes have become a prevalent mode of education, especially in the past two years. These digital platforms offer several advantages. First, they provide flexibility, allowing students to learn from the comfort of their homes. This is especially beneficial for those with busy schedules or who are studying part-time. 

Second, online classes often offer a wider range of courses, enabling learners to explore diverse subjects. Additionally, these classes promote self-discipline and time management skills as students must regulate their own study routines.

However, there are challenges associated with online learning. Technical issues can disrupt classes, and the lack of face-to-face interaction may hinder social development. It can also be isolating for some students.

In conclusion, online classes offer convenience and a variety of courses, but they also present challenges related to technology and socialization. The future of education likely involves a blend of traditional and online learning methods, catering to diverse learning needs.

Also Read: Online Learning

Essay on Online Classes in 200 Words

Online classes have become a prevalent mode of education. However, this shift has brought about both advantages and challenges.

One significant benefit of online classes is accessibility. They allow students from diverse backgrounds and locations to access quality education without any constraints. This inclusivity promotes diversity and global learning experiences. Additionally, online classes often offer flexible schedules, enabling students to balance their studies with other responsibilities.

However, online classes present challenges too. Technical issues and a lack of face-to-face interaction can hinder effective learning. Students may even struggle with self-discipline and motivation, leading to a decline in academic performance. Moreover, the absence of physical facilities like libraries and laboratories can limit hands-on learning opportunities.

In conclusion, online classes have revolutionized education by providing accessibility and flexibility. Yet, they also pose challenges related to technical issues, motivation, and practical experiences. 

Related Articles

Every student has their own pace of study, and this is where distance learning’s benefits really shine. You can go at your own speed in online classes, go over the material as needed, and complete the work in a method that best suits your learning preferences.

Online courses can be successful provided they are well-designed and delivered, just like any other course or programme. However, this depends from person to person as not every student is meant for online classes. 

In online education, students get to study online using a computer/laptop and only need a proper internet connection. 

For more information on such interesting topics, visit our essay-writing page and follow Leverage Edu ! 

Malvika Chawla

Malvika is a content writer cum news freak who comes with a strong background in Journalism and has worked with renowned news websites such as News 9 and The Financial Express to name a few. When not writing, she can be found bringing life to the canvasses by painting on them.

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Online Classes Vs. Traditional Classes Essay

Online vs. in-person classes essay – introduction, online and traditional classes differences, works cited.

The article compares and contrasts online classes and traditional classes. Among the advantages of online classes are flexibility and convenience, while in-person classes offer a more structured learning environment. The author highlights that online lessons can be more cost-effective, although they lack support provided by live interactions. Overall, the online vs. traditional classes essay is very relevant today, and the choice depends on the individual student’s needs and preferences.

Modern technology has infiltrated the education sector and as a result, many college students now prefer taking online classes, as opposed to attending the traditional regular classes. This is because online classes are convenient for such students, and more so for those who have to both work and attend classes.

As such, online learning gives them the flexibility that they needed. In addition, online learning also gives an opportunity to students and professionals who would not have otherwise gone back to school to get the necessary qualifications. However, students who have enrolled for online learning do not benefit from the one-on-one interaction with their peers and teachers. The essay shall endeavor to examine the differences between online classes and the traditional classes, with a preference for the later.

Online classes mainly take place through the internet. As such, online classes lack the regular student teacher interaction that is common with traditional learning. On the other hand, learning in traditional classes involves direct interaction between the student and the instructors (Donovan, Mader and Shinsky 286).

This is beneficial to both the leaner and the instructors because both can be bale to establish a bond. In addition, student attending the traditional classroom often have to adhere to strict guidelines that have been established by the learning institution. As such, students have to adhere to the established time schedules. On the other hand, students attending online classes can learn at their own time and pace.

One advantage of the traditional classes over online classes is that students who are not disciplined enough may not be able to sail through successfully because there is nobody to push them around. With traditional classes however, there are rules to put them in check. As such, students attending traditional classes are more likely to be committed to their education (Donovan et al 286).

Another advantage of the traditional classes is all the doubts that students might be having regarding a given course content can be cleared by the instructor on the spot, unlike online learning whereby such explanations might not be as coherent as the student would have wished.

With the traditional classes, students are rarely provided with the course materials by their instructors, and they are therefore expected to take their own notes. This is important because they are likely to preserve such note and use them later on in their studies. In contrast, online students are provided with course materials in the form of video or audio texts (Sorenson and Johnson 116).

They can also download such course materials online. Such learning materials can be deleted or lost easily compared with handwritten class notes, and this is a risk. Although the basic requirements for a student attending online classes are comparatively les in comparison to students attending traditional classes, nonetheless, it is important to note that online students are also expected to be internet savvy because all learning takes place online.

This would be a disadvantage for the regular student; only that internet savvy is not a requirement. Students undertaking online learning are likely to be withdrawn because they hardly interact one-on-one with their fellow online students or even their instructors. The only form of interaction is online. As such, it becomes hard for them to develop a special bond with other students and instructors. With traditional learning however, students have the freedom to interact freely and this helps to strengthen their existing bond.

Online learning is convenient and has less basic requirements compared with traditional learning. It also allows learners who would have ordinarily not gone back to school to access an education. However, online students do not benefit from a close interaction with their peers and instructors as do their regular counterparts. Also, regular students can engage their instructors more easily and relatively faster in case they want to have certain sections of the course explained, unlike online students.

Donovan, Judy, Mader, Cynthia and Shinsky, John. Constructive student feedback: Online vs. traditional course evaluations. Journal of Interactive Online Learning , 5.3(2006): 284-292.

Sorenson, Lynn, and Johnson, Trav. Online Student Ratings of Instructions . San Francisco: Jossey Bass, 2003. Print.

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The COVID-19 pandemic has changed education forever. This is how 

With schools shut across the world, millions of children have had to adapt to new types of learning. Image:  REUTERS/Gonzalo Fuentes

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Farah lalani.

.chakra .wef-9dduvl{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;}@media screen and (min-width:56.5rem){.chakra .wef-9dduvl{font-size:1.125rem;}} Explore and monitor how .chakra .wef-15eoq1r{margin-top:16px;margin-bottom:16px;line-height:1.388;font-size:1.25rem;color:#F7DB5E;}@media screen and (min-width:56.5rem){.chakra .wef-15eoq1r{font-size:1.125rem;}} Education, Gender and Work is affecting economies, industries and global issues

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Stay up to date:, education, gender and work.

• The COVID-19 has resulted in schools shut all across the world. Globally, over 1.2 billion children are out of the classroom.
• As a result, education has changed dramatically, with the distinctive rise of e-learning, whereby teaching is undertaken remotely and on digital platforms.
• Research suggests that online learning has been shown to increase retention of information, and take less time, meaning the changes coronavirus have caused might be here to stay.

While countries are at different points in their COVID-19 infection rates, worldwide there are currently more than 1.2 billion children in 186 countries affected by school closures due to the pandemic. In Denmark, children up to the age of 11 are returning to nurseries and schools after initially closing on 12 March , but in South Korea students are responding to roll calls from their teachers online .

With this sudden shift away from the classroom in many parts of the globe, some are wondering whether the adoption of online learning will continue to persist post-pandemic, and how such a shift would impact the worldwide education market.

Even before COVID-19, there was already high growth and adoption in education technology, with global edtech investments reaching US$18.66 billion in 2019 and the overall market for online education projected to reach $350 Billion by 2025 . Whether it is language apps , virtual tutoring , video conferencing tools, or online learning software , there has been a significant surge in usage since COVID-19.

How is the education sector responding to COVID-19?

In response to significant demand, many online learning platforms are offering free access to their services, including platforms like BYJU’S , a Bangalore-based educational technology and online tutoring firm founded in 2011, which is now the world’s most highly valued edtech company . Since announcing free live classes on its Think and Learn app, BYJU’s has seen a 200% increase in the number of new students using its product, according to Mrinal Mohit, the company's Chief Operating Officer.

Tencent classroom, meanwhile, has been used extensively since mid-February after the Chinese government instructed a quarter of a billion full-time students to resume their studies through online platforms. This resulted in the largest “online movement” in the history of education with approximately 730,000 , or 81% of K-12 students, attending classes via the Tencent K-12 Online School in Wuhan.

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The future of jobs report 2023, how to follow the growth summit 2023.

Other companies are bolstering capabilities to provide a one-stop shop for teachers and students. For example, Lark, a Singapore-based collaboration suite initially developed by ByteDance as an internal tool to meet its own exponential growth, began offering teachers and students unlimited video conferencing time, auto-translation capabilities, real-time co-editing of project work, and smart calendar scheduling, amongst other features. To do so quickly and in a time of crisis, Lark ramped up its global server infrastructure and engineering capabilities to ensure reliable connectivity.

Alibaba’s distance learning solution, DingTalk, had to prepare for a similar influx: “To support large-scale remote work, the platform tapped Alibaba Cloud to deploy more than 100,000 new cloud servers in just two hours last month – setting a new record for rapid capacity expansion,” according to DingTalk CEO, Chen Hang.

Some school districts are forming unique partnerships, like the one between The Los Angeles Unified School District and PBS SoCal/KCET to offer local educational broadcasts, with separate channels focused on different ages, and a range of digital options. Media organizations such as the BBC are also powering virtual learning; Bitesize Daily , launched on 20 April, is offering 14 weeks of curriculum-based learning for kids across the UK with celebrities like Manchester City footballer Sergio Aguero teaching some of the content.

What does this mean for the future of learning?

While some believe that the unplanned and rapid move to online learning – with no training, insufficient bandwidth, and little preparation – will result in a poor user experience that is unconducive to sustained growth, others believe that a new hybrid model of education will emerge, with significant benefits. “I believe that the integration of information technology in education will be further accelerated and that online education will eventually become an integral component of school education,“ says Wang Tao, Vice President of Tencent Cloud and Vice President of Tencent Education.

There have already been successful transitions amongst many universities. For example, Zhejiang University managed to get more than 5,000 courses online just two weeks into the transition using “DingTalk ZJU”. The Imperial College London started offering a course on the science of coronavirus, which is now the most enrolled class launched in 2020 on Coursera .

Many are already touting the benefits: Dr Amjad, a Professor at The University of Jordan who has been using Lark to teach his students says, “It has changed the way of teaching. It enables me to reach out to my students more efficiently and effectively through chat groups, video meetings, voting and also document sharing, especially during this pandemic. My students also find it is easier to communicate on Lark. I will stick to Lark even after coronavirus, I believe traditional offline learning and e-learning can go hand by hand."

These 3 charts show the global growth in online learning

The challenges of online learning.

There are, however, challenges to overcome. Some students without reliable internet access and/or technology struggle to participate in digital learning; this gap is seen across countries and between income brackets within countries. For example, whilst 95% of students in Switzerland, Norway, and Austria have a computer to use for their schoolwork, only 34% in Indonesia do, according to OECD data .

In the US, there is a significant gap between those from privileged and disadvantaged backgrounds: whilst virtually all 15-year-olds from a privileged background said they had a computer to work on, nearly 25% of those from disadvantaged backgrounds did not. While some schools and governments have been providing digital equipment to students in need, such as in New South Wales , Australia, many are still concerned that the pandemic will widenthe digital divide .

Is learning online as effective?

For those who do have access to the right technology, there is evidence that learning online can be more effective in a number of ways. Some research shows that on average, students retain 25-60% more material when learning online compared to only 8-10% in a classroom. This is mostly due to the students being able to learn faster online; e-learning requires 40-60% less time to learn than in a traditional classroom setting because students can learn at their own pace, going back and re-reading, skipping, or accelerating through concepts as they choose.

Nevertheless, the effectiveness of online learning varies amongst age groups. The general consensus on children, especially younger ones, is that a structured environment is required , because kids are more easily distracted. To get the full benefit of online learning, there needs to be a concerted effort to provide this structure and go beyond replicating a physical class/lecture through video capabilities, instead, using a range of collaboration tools and engagement methods that promote “inclusion, personalization and intelligence”, according to Dowson Tong, Senior Executive Vice President of Tencent and President of its Cloud and Smart Industries Group.

Since studies have shown that children extensively use their senses to learn, making learning fun and effective through use of technology is crucial, according to BYJU's Mrinal Mohit. “Over a period, we have observed that clever integration of games has demonstrated higher engagement and increased motivation towards learning especially among younger students, making them truly fall in love with learning”, he says.

A changing education imperative

It is clear that this pandemic has utterly disrupted an education system that many assert was already losing its relevance . In his book, 21 Lessons for the 21st Century , scholar Yuval Noah Harari outlines how schools continue to focus on traditional academic skills and rote learning , rather than on skills such as critical thinking and adaptability, which will be more important for success in the future. Could the move to online learning be the catalyst to create a new, more effective method of educating students? While some worry that the hasty nature of the transition online may have hindered this goal, others plan to make e-learning part of their ‘new normal’ after experiencing the benefits first-hand.

The importance of disseminating knowledge is highlighted through COVID-19

Major world events are often an inflection point for rapid innovation – a clear example is the rise of e-commerce post-SARS . While we have yet to see whether this will apply to e-learning post-COVID-19, it is one of the few sectors where investment has not dried up . What has been made clear through this pandemic is the importance of disseminating knowledge across borders, companies, and all parts of society. If online learning technology can play a role here, it is incumbent upon all of us to explore its full potential.

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The views expressed in this article are those of the author alone and not the World Economic Forum.

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Essay by UW–Madison’s Jones shares origins of ‘These Grand Places’ photography project

UW–Madison’s Tomiko Jones, an assistant professor in the School of Education’s Art Department, has written an essay about her long-form photography-based project, “These Grand Places,” for the digital magazine Edge Effects .

Jones’ photography and multidisciplinary installations explore social, cultural, and geopolitical transitions, considering the twin crises of too much and too little in the age of climate change. Running themes within her work include ecological concerns, questions of belonging, and activated cultural traditions.

In the essay, “Imagining National Belonging in American Landscapes,” Jones discusses the origins of “These Grand Places” in the road trips of her childhood and in her lived experience as a person of multiracial identity. “From elementary school to graduate studies, I often could not find myself in the history I was taught, the artwork I saw, or the archives I studied,” Jones writes. “The desire to repair the feeling of being outside official histories and search for a sense of belonging has ultimately defined my creative practice and shaped my scholarly research.”

“My project ‘These Grand Places’ arguably began during my graduate studies as an investigation into the construct of identifying land as ‘ours’,” Jones adds. “Photography, as an invention and tool, played a significant role in Manifest Destiny, in hegemonic narratives of how the nation was made, identified, and ‘conquered’.”

Jones notes the project, which got its start through a Seed Grant through the School of Education’s Grand Challenges grant program, was also informed by her participation in the First Nations Cultural Landscape Tour , led by Omar Poler.

During their walk, Jones writes, Poler asked the question: “What if we were to look at this place in a different way, through new eyes? How would that change how we acted?”

She adds that this question introduced a new philosophical idea to explore in her work: “How do we see differently?”

“These Grand Places” will premiere in a multimedia installation in Jan. 2025 as part of Jones’ mid-career exhibition.

To learn more, read Jones’ essay, “ Imagining National Belonging in American Landscapes .”

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The effect of students’ online learning experience on their satisfaction during the COVID-19 pandemic: The mediating role of preference

1 Department of Pedagogy, School of Teacher Education, Jiangsu University, Zhenjiang, Jiangsu, China

Flavian Adhiambo Odhiambo

Dickson kofi wiredu ocansey.

2 Directorate of University Health Services, University of Cape Coast, PMB, Cape Coast, Ghana

Associated Data

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Introduction

During the peak of the COVID-19 pandemic, nearly all educational institutions globally had to eventually embrace the maneuver of transferring to nearly 100% online learning as a new routine for different curricula. Although many students in developing countries such as Kenya are only experiencing the exclusive online learning approach for the first time, research on students’ experience and satisfaction with COVID-19-imposed online learning is largely lacking. Thus, this study examined the effect of online-learning experiences on satisfaction in the setting of the COVID-19 pandemic in Kenya. The mediating role of students’ preference on the relationship between online-learning experience and satisfaction was also examined.

A web-based survey involving 501 respondents was analyzed using IBM® SPSS® and AMOS software platforms. A structural equation model (SEM) was used to analyze the relationships.

Results and Discussion

Results showed that 80% of participants indicated their preference for in-person learning as against 20% for online learning. Students’ satisfaction-SS had a significant positive correlation with online classroom perceived quality-OCPQ, acquisition of self-confidence-ASC, teaching performance and engagement-TPE, and preference for online learning-POL but a negative correlation with internet access and cost-IAC. Moreover, while POL positively correlated with OCPQ, ASC, and TPE, it negatively correlated with IAC. Both the structural model for the main effect and the mediation model provided a good fit and confirmed these relationships. Student preference had a significant effect on satisfaction and played a significant mediating role in the relationship between online-learning experience and satisfaction. These findings shed light on the underlying factors that explain students’ online learning satisfaction and provide guidelines for universities and policymakers to make better decisions that enhance students’ online-learning experience and satisfaction.

1. Introduction

The media and means through which people embark on learning are fundamentally changing as technology constantly expands, increasing its impacts on the ways people acquire, correct, and update their understanding ( Lodge and Harrison, 2019 ). In effect, new and evolving technologies present immense opportunities for teaching and learning ( Szopiński and Bachnik, 2021 ). The emergence of mobile network devices including cell phones and personalized computers indicates that information can be accessed anywhere and anytime with an Internet connection. This new and constantly evolving information reality carries along a substantial affordance for learning both in informal and formal education settings. In the phase of this development, the world has recently been confronted with a devastating plague, the COVID-19 pandemic, taking a full toll globally in 2020. The COVID-19 situation necessitates embracing new pandemic-imposed conditions, such as distance learning, which need to be addressed by the higher education sector as well. Both students and faculty members were compelled to re-think the application of available technology resources to not only deliver higher education services but also benefit from them as well. However, a critical question remains, as to whether this new setup is effective and satisfies the student’s learning needs.

The COVID-19 pandemic greatly affected all areas of life, including education, as educational institutions were locked down ( Khalil et al., 2020 ). Online classes provided a safe and secure means of engaging with students to continue learning, hence, almost all higher education institutions globally, shifted to online-classroom learning. This huge unanticipated transition from the traditional on-sight learning approach to an exclusively online learning setup presented a new phase of teaching methods across educational institutions in delivering the course content to their students ( Xu and Jaggars, 2011 ). Unlike higher education students in developed countries, who are already exposed to online textbooks and modules with video lectures and computer-based exams in the 21st century, many students in low-economy countries like Kenya are new to online-classroom learning. Online learning can be challenging for students who are being exposed to it for the first time, and also because of the limited non-verbal communication. Other aspects of the online classroom, such as student and instructor interactions or engagement, accessibility of learning materials, internet access and cost, perceived quality, self-confidence, and time management, can equally influence the overall experience of online education participants and their satisfaction ( Appleton-Knapp and Krentler, 2006 ; Kuo et al., 2014 ; She et al., 2021 ; Conrad et al., 2022 ). It is therefore important to assess this evolution in teaching modalities, to provide policymakers with data meant to improve the teaching and learning process in these odd periods.

Regardless of the fact that many students in developing countries such as Kenya are only experiencing the exclusive online learning approach for the first time, research on students’ experience and satisfaction is largely lacking. It is expected that the abrupt introduction of the online classroom would present several challenges that bother on issues such as internet accessibility and affordability, perceived quality of the learning process and activities, teacher-student engagement, as well as overall students satisfaction, all of which influence the success of this learning method. Therefore, there is a need to assess the effect of students’ online-learning experience on their satisfaction in Kenya during the COVID-19 pandemic. Moreover, since the COVID-19 outbreak resulted in a sudden dramatic change that left no time and space for preparing students to adopt and accept online learning (as against traditional on-sight classroom learning), students’ preference for online learning could significantly influence their overall satisfaction with online-learning sections. Thus, examining the direct and mediating effect of students’ preference on their online-learning satisfaction could emphasize the need to properly and adequately orient students to accept online learning as an alternative to the traditional on-sight learning approach. This study assesses the effect of the online learning experience of university students on their satisfaction in the setting of the COVID-19 pandemic in Kenya. The mediating role of students’ preference on the relationship between online-learning experience and students’ satisfaction is also examined. This is expected to provide means of improving e-learning and maximizing students’ satisfaction in the phase of both pandemic and normal times.

2. Literature review and hypotheses development

Online learning is a global trend in higher education, particularly in the context of the corona virus disease (COVID-19) pandemic. Scholars from different countries and institutions are eagerly exploring innovative online teaching and learning strategies intending to enhance student achievement in terms of perceived learning satisfaction and engagement ( Muzammil et al., 2020 ; Yousaf et al., 2022 ). Previous studies have suggested that heightened learning satisfaction may lead to further engagement, which is a significant predictor of learning outcomes ( Yousaf et al., 2022 ). Students’ satisfaction is influenced by varying factors from two main sources, which are personal and institutional factors ( Marzo Navarro et al., 2005 ; Appleton-Knapp and Krentler, 2006 ). The personal factors cover individual aspects such as age, gender, preferred learning method, and student GPA, while the institutional factors cover the quality of instructions, the quality of the classroom, course content and learning materials, promptness and quality of the instructor’s feedback, teaching style, available learning equipment, and clarity of expectation. Moreover, the effective use of technology, the quality of lecturers, and the quality of physical facilities as key determinants of student satisfaction ( Wilkins and Stephens Balakrishnan, 2013 ). Other factors such as lecturer-student relationship, interaction with fellow students, teaching ability, and flexible curriculum influence learners’ preference and satisfaction ( Fisher et al., 2021 ; Kim and Kim, 2021 ). Previous studies on e-Learning, including that of Unver et al. (2017) , Szopiński and Bachnik (2021) , and Milicevic et al. (2021) used different aspects of students’ online-learning experience such as teaching performance and engagement, acquisition of self-confidence, internet access and cost and perceived quality to assess the overall students’ satisfaction with online-learning ( Unver et al., 2017 ; Milicevic et al., 2021 ; Szopiński and Bachnik, 2021 ). Other studies have also examined the relationship between students’ preference for online learning and their satisfaction during the COVID-19 pandemic ( Segbenya et al., 2022 ). Several studies have proposed theories that explain the relationship between student experience and satisfaction in an effort to better understand the psycho-social dynamics of student satisfaction, including the expectation confirmation theory ( Oliver, 1977 , 1980 ), happy-productive” student theory ( Cotton et al., 2002 ), and investment model theory ( Hatcher et al., 1992 ). This study employed the expectation confirmation theory to explore the relationship between students’ online learning experience and their satisfaction. As a theoretical approach based on consumer satisfaction, the expectation confirmation theory considers satisfaction as a function of the extent to which students’ expectations about online learning are met, with positive confirmations resulting in higher levels of satisfaction ( Jiang and Klein, 2009 ).

2.1. The effect of teaching performance and engagement on students’ satisfaction

Tertiary institutions remain committed to enhancing students’ learning outcomes, which have always been evaluated in terms of student engagement, performance, and satisfaction ( Fisher et al., 2021 ). Student engagement is characterized by the degrees of attention, interest, participation, curiosity, optimism, belonging, passion, in-depth learning, interaction, and a sense of autonomy and control experienced by students ( Deschaine and Whale, 2017 ; Fisher et al., 2021 ). Engagement involves more than participation in an activity; it also includes feelings, emotions, and finding value in an experience. Therefore, student engagement involves expending effort and time on learning ( Alaulamie, 2014 ). Learning satisfaction which positively correlates with learning engagement is identified as a key indicator of a student’s enjoyment of their studies, where engagement serves as an essential construct for academic success ( Bond et al., 2021 ; Fisher et al., 2021 ). Thus, sufficient teaching engagement leads to increased students satisfaction ( Kim and Kim, 2021 ; She et al., 2021 ; Yousaf et al., 2022 ). It is further demonstrated that the instructor’s ability to deliver quality E-learning (quality teaching performance) affects students’ satisfaction ( Pham et al., 2019 ). Quality teaching includes sufficient teacher-student engagement. Studies show the importance of quality learner-instructor interaction as two-way communication between the instructor and students ( Alqurashi, 2019 ), and is linked with teaching performance. Besides, it is documented that learner-content engagement or interaction is the most important predictor of student satisfaction ( Kuo et al., 2014 ; Alqurashi, 2019 ). Thus, teaching performance and engagement significantly influence students’ satisfaction and serve as critical markers of effective teaching and students’ satisfaction ( Carpenter et al., 2020 ) and vice versa ( Yılmaz and Yılmaz, 2022 ). From the perspective of the expectation confirmation theory, students expect better teaching performance and engagement in their online classes, and the degree to which demand is met influences heir satisfaction ( Jiang and Klein, 2009 ).

2.2. The effect of internet accessibility and cost on student satisfaction

Students use the internet daily to access information, gather data, and conduct research. In the phase of the COVID-19 pandemic, internet usage became the only option for most educational facilities owing to the lockdown of entire regions and cities ( Imsa-ard, 2020 ; Bond et al., 2021 ). Despite the wide adoption of online learning in higher education during the COVID-19 pandemic, several factors that negatively influence students’ satisfaction with this novel learning environment, such as internet accessibility and affordability, still remain in many countries, as studies indicate differences in student access to digital learning resources while at home, including high-quality broadband connectivity ( Rasheed et al., 2020 ; Cullinan et al., 2021 ). Students who experience internet connectivity problems such as network congestion during online learning are found to poorly rate their e-Learning experience and their overall satisfaction ( Li et al., 2021 ). Internet cost and accessibility remain a challenge globally, even in developed countries. For example, a study in the US estimated that 20% of college students had difficulty maintaining access to technology due to internet connectivity problems and data limitations (affordability) ( Gonzales et al., 2020 ). The challenge of internet affordability and accessibility is driven by a range of factors, including financial constraints, gaps in access to appropriate equipment such as a laptop or desktop personal computer, and the digital literacy skills required to engage with online learning ( Silva et al., 2018 ). Variations in connectivity constrain student engagement in online class and with online content, invariably affecting students’ performance and satisfaction ( Gonzales et al., 2020 ). On the background of the expectation confirmation theory, students anticipate smooth internet connectivity that is also cost-effective to have a posiive learning experience, which inturn increases satisfaction ( Oliver, 1980 ; Jiang and Klein, 2009 ).

2.3. The relationship between perceived quality and student satisfaction

In addition to students’ preference and teaching performance and engagement, perceived quality (perception of online learning being well delivered), is reported to be highly important in determining the students’ satisfaction ( Ho et al., 2021 ). Students are more satisfied with online learning if they generally perceive an online course as quality, appropriate, and like the online course, or somewhat familiar with the course background ( Beqiri et al., 2009 ). A study conducted during the COVID-19 pandemic lockdown in Thailand found that, regardless of the abrupt move from traditional classrooms to online learning, students’ expectancy of the quality (perceived quality) of the newly introduced learning system was matched with the traditional face-to-face learning and influenced their satisfaction. Therefore, the perceived quality of the online-learning system forms a significant part of overall student satisfaction ( Kornpitack and Sawmong, 2022 ). Interestingly, students’ perception of quality teaching remains an essential part of their learning experiences in school and later in life ( Muvui Muya, 2019 ). Therefore, students’ satisfying experience with traditional on-sight learning might cause them to highly expect quality teaching and learning experiences from the online-learning platforms, thus contributing to their overall satisfaction. Drawing on the expectation confirmation theory, perceived quality as an expectation construct, will influence perceived performance and attract either a positive or negative evaluation (disconfirmation of beliefs), invariably affecting satisfaction ( Oliver, 1977 , 1980 ).

2.4. Self-confidence and student satisfaction

Students’ confidence in online leaning was reported as the strongest positive predictor of both students’ satisfaction and perceived quality or usefulness of online classes ( Landrum, 2020 ). Self-confidence is defined as one’s belief in his/her ability to perform best, capacity to maximize self-faith, and believing in self-worth, and serves as a crucial determinant of academic performance ( Ballane, 2019 ). Students with high self-confidence turn to welcome new challenges and have a greater desire to learn. It is reported that students need not only the knowledge of the subject to reach their learning objectives in e-learning but also self-confidence ( Kaleci and Akleman, 2019 ). Since the pandemic-imposed changes affect the psychological well-being of students ( Villani et al., 2021 ), where online learning poses threats to self-confidence as it could instill fear, disappointment, and shame ( Blanco et al., 2020 ), the acquisition of self-confidence would influence students satisfaction. Self-efficacy, which also reflects self-confidence in online learning, refers to one’s confidence to use the necessary gadget and the internet to search for information ( Landrum, 2020 ), and positively correlates with students’ online learning satisfaction ( Kirmizi, 2015 ; Yilmaz, 2017 ; Hammouri and Abu-Shanab, 2018 ) as well as their perceived ease of use, quality, and usefulness ( Chen et al., 2020 ). Other studies on the COVID-19 outbreak report the direct and indirect influence of self-efficacy and the perceived ease of use and usefulness of online platforms on students’ satisfaction ( Jiang et al., 2021 ). Thus, students’ e-learning self-confidence and readiness are significant predictors of their satisfaction and motivation ( Yilmaz, 2017 ). Drawing on the expectation confirmation theory, acquisition of self-confidence will lead to a positive disconfirmation, which is posited to increase post-online learning or post-adoption satisfaction ( Oliver, 1977 , 1980 ; Jiang and Klein, 2009 ).

2.5. Effect of students’ online learning experience on preference

Student online learning experience, including poor internet access and connectivity, discomfort, and lack of familiarity with the technology, negatively influence students’ preference for online learning ( DeLone and McLean, 2003 ; Al-Fraihat et al., 2020 ). A survey carried out in 2020 that focused on technological issues and challenges during the transition to online learning, found that the lack of readiness coupled with internet access issues was directly associated with the online-learning system quality, and significantly influenced student satisfaction ( EDUCAUSE, 2020 ), as assumed in the original model of Delone and Mclean ( DeLone and McLean, 2003 ; Al-Fraihat et al., 2020 ). Similarly, other literature suggests that improved system quality positively influences student preference and satisfaction when E-learning ( Cidral et al., 2018 ; Al-Fraihat et al., 2020 ). Self-efficacy, which also reflects self-confidence in one’s ability, is defined as the individuals’ belief in their own capability to perform a certain task, challenge, or successfully engage with educational technology influences students’ readiness and preference for online-learning technologies ( Eom, 2012 ; Patricia Aguilera-Hermida, 2020 ), and has been shown to be interconnected with student satisfaction levels ( Wang and Degol, 2014 ). Self-efficacy is affected by online platform content and accessibility, which in turn, positively influence student satisfaction ( Prifti, 2022 ). Moreover, in the application of technology in teaching and learning, adequate orientation and training of students and faculty in remote learning and teaching may enhance preference ( Muthuprasad et al., 2021 ), as indicated in recent reports of increased students’ preference for online leaning ( Jenay, 2022 ). The success in employing e-learning is also associated with quality teaching performance, an interactive teaching style, and attitudes of the teacher, as well as the attitudes and experiences of students with respect to technology ( Linjawi, 2010 ), all of which are influenced by preference. From the perspective of the expectation confirmation theory, if students online learning expectations are met, it will positively influence their preference for the online learning classroom and vice versa ( Jiang and Klein, 2009 ).

2.6. The relationship between student preference and satisfaction

It is reported that students’ preference for either online learning or on-sight learning significantly influences their overall satisfaction with learning. For example, a study found that although the majority of students were competent in technology and had no obvious challenge in accessing learning devices or Wi-Fi during the COVID-19 pandemic, they simply preferred face-to-face learning to online learning, and this preference was found to be the most important predictor of students’ satisfaction ( Ho et al., 2021 ). Since most students were only engaged in the traditional face-face teaching and learning process before the pandemic, the lack of adequate orientation and ample time to adjust to the online-learning process leads to less preference and lack of satisfaction ( Karadag et al., 2021 ). This is also asserted by other researchers who indicate that typically online learning is regarded as a well-planned system from the beginning and may go through a lengthy designed process ( Charles et al., 2020 ), however, the online teaching and learning systems being employed in many countries were hurried to provide a shift in instructional delivery due to the COVID-19 crisis ( Cameron-Standerford et al., 2020 ; Rahiem, 2020 ). Therefore, decreased preference or readiness negatively influences satisfaction ( Rahiem, 2020 ). Drawing on the expectation confirmation theory, increased preference will correlate with a positive evaluation or disconfirmation of beliefs, and will lead to increased satisfaction ( Oliver, 1977 , 1980 ).

In summary, students have been forced into online classrooms due to the COVID-19 pandemic. On the phase of the implication, several factors can undermine the success of online learning, thus it is important to assess and understand the perspective of the student regarding their experience with online learning during the pandemic, and how it influences their overall satisfaction with online learning.

2.7. Conceptual framework

The theoretical foundation of our framework is based on the Expectation Confirmation Theory by Richard Oliver, which is a cognitive theory that seeks to explain post-adoption satisfaction as a function of users’ expectations, perceived performance, and disconfirmation of beliefs. Thus, the primary construct of this theory are expectations, perceived performance, disconfirmation of beliefs, and satisfaction ( Oliver, 1977 , 1980 ). Expectations refer to users’ anticipated or predicted attributes associated with the service or technology artifact and directly affect both perceived performance (users’ perceptions of the actual performance of a service or technology artifact) and disconfirmation of beliefs (service or technology artifact evaluation or judgment) and indirectly affect post-adoption satisfaction by way of a mediational relationship through the disconfirmation construct ( Oliver, 1977 , 1980 ; Bhattacherjee, 2001 ). In the light of the Expectation Confirmation Theory, users’ expectations and perceived performance of the online learning platform constitute the students’ online learning experience (perceived quality of online classroom learning, teaching performance and engagement, internet access and cost, and acquisition of self-confidence), which influence their disconfirmation (preference). The disconfirmation of beliefs, herein represented by preference, as an evaluation of the online learning service produces either a positive or negative response, which in turn influences users’ satisfaction ( Bhattacherjee, 2001 ). In addition, users experience with the online learning directly influences their satisfaction ( Figure 1 ).

The conceptual framework of the study.

Moreover, several factors have been reported that identify and influence students’ online learning satisfaction ( Al-Fraihat et al., 2020 ). An earlier online-learning study model proposed by DeLone and McLean (2003) , primarily considered factors such as the quality of information systems and services that determine learner satisfaction ( DeLone and McLean, 2003 ). This model has been used to assess online-learning success among students in universities during the COVID-19 pandemic ( Shahzad et al., 2021 ). Moreover, the user satisfaction approach ( Al-Fraihat et al., 2020 ), as a theoretical framework in the assessment of E-learning, has been adopted by researchers to measure the learners’ satisfaction during their online learning experience in higher education in developing countries ( Yawson and Yamoah, 2020 ). Other E-learning quality and technology acceptance models have been developed with an emphasis on user experience that culminates into the platform’s usefulness and ease of use ( Abdullah and Ward, 2016 ; Al-Fraihat et al., 2020 ). Conceptual models based on a semi-structured questionnaire conducting thematic analyses of college students’ online learning experience during the COVID-19 pandemic ( Shim and Lee, 2020 ), as well as tailor-made questionnaires that measure student satisfaction using 5-point Likert-scale questions ( Alqurshi, 2020 ) have been developed.

However, most of the available theoretical models are built to assess pre-planned E-learning but not the abruptly imposed online learning seen during the COVID-19 pandemic, thus, their direct application may not suitably reflect underlying factors influencing the satisfaction and success of the COVID-19-induced emergency remote learning. Therefore, some researchers have developed other frameworks, such as a tailor-made survey kit by EDUCAUSE that allows institutions to rapidly adopt to gather feedback from higher education stakeholders ( EDUCAUSE, 2020 ). This gives room for framework formation that is carefully tailed to adequately assess the online-learning situation in Kenya while taking reference from the components of the multidimensional EESS (Evaluating E-learning System Success) model. Therefore, in this study, students’ online study experience was assessed by considering some of the key factors that influence effective e-Learning, including internet access and cost, teaching performance and engagement, perceived quality of online classroom learning, and acquisition of self-confidence ( Unver et al., 2017 ; Milicevic et al., 2021 ; Szopiński and Bachnik, 2021 ). In the light of the multidimensional EESS model, teaching performance and engagement falls under instructor quality, students’ self-confidence and preference under learner quality, internet access and cost under information quality – accessibility, and perceived quality of online learning under educational system quality ( Al-Fraihat et al., 2020 ). These factors together shape a student’s learning experience and lead to the overall satisfaction of the entire learning process. Thus, the effect of students’ online study experience on their satisfaction was examined. Moreover, the influence of students’ preferences on their satisfaction was assessed. Finally, the mediating role of students’ preference on the relationship between their online-learning experience and satisfaction was examined ( Figure 1 ).

2.8. Hypothesis

From the background reviewed in the literature and the conceptual framework ( Figure 1 ), four main hypotheses were deduced for the study.

HI : There is a direct significant relationship between students’ online learning experience and their overall satisfaction with online classes during the COVID-19 pandemic.

H1a : Internet access and cost influence students’ overall satisfaction with online classes during the COVID-19 pandemic.

H1b : Teaching performance and engagement have a significant positive influence on students’ overall satisfaction with online classes during the COVID-19 pandemic.

H1c : Students’ acquisition of self-confidence through online learning during the COVID-19 pandemic has a significant positive influence on students’ overall satisfaction.

H1d : Students’ perceived quality of online learning has a significant positive influence on students’ overall satisfaction during the COVID-19 pandemic.

H2 : Students’ online-learning experience significantly correlates with their preference for online learning during the COVID-19 pandemic.

H2a : Internet access and cost influence students’ preference for online learning during the COVID-19 pandemic.

H2b : Teaching performance and engagement have a significant positive influence on students’ preference for online learning during the COVID-19 pandemic.

H2c : Students’ acquisition of self-confidence through online learning during the COVID-19 pandemic has a significant positive influence on students’ preference for online learning.

H2d : Students’ perceived quality of online learning has a significant positive influence on students’ preference for online learning during the COVID-19 pandemic.

H3 : Students’ preference for online learning has a significant positive influence on students’ overall satisfaction during the COVID-19 pandemic.

H4 : Students’ preference for online learning mediates the effect of students' online learning experience on their overall satisfaction during the COVID-19 pandemic.

H4a : Students’ preference for online learning mediates the effect of internet access and cost on their overall satisfaction with online classes during the COVID-19 pandemic.

H4b : Students’ preference for online learning mediates the effect of teaching performance and engagement on their overall satisfaction with online classes during the COVID-19 pandemic.

H4c : Students’ preference for online learning mediates the effect of students’ acquisition of self-confidence through online learning on their overall satisfaction during the COVID-19 pandemic.

H4d : Students’ preference for online learning mediates the effect of students’ perceived quality of online learning on their overall satisfaction during the COVID-19 pandemic.

3. Materials and methods

3.1. research design and study area.

A cross-sectional study design was used to obtain the primary data from university students between January 2021 and June 2021. Participants were selected from universities in Nairobi, also known as the safari capital of Africa, and serves as the capital and largest city of Kenya.

3.2. Sampling technique

The study employed the convenient simple random sampling approach as it is considered reliable, fair, and effective. A survey form was prepared by using the Microsoft Form web-based survey technology and that access link was distributed among students in the selected universities. Participants received the survey link through social networks such as WhatsApp and Instagram, which contained clearly outlined questions and instructions. Respondents could take part and complete the questionnaire at any time of their convenience.

3.3. Sample population and size

Participants were recruited among students from three selected universities in Nairobi, i.e., the Kenyatta University, The Technical University of Kenya, and the Jomo Kenyatta University of Agriculture and Technology. A total of 501 respondents made up of males and females who were willing to participate were recruited. The criteria for selection included only students who have experienced or currently experiencing online-classroom learning owing to the COVID-19 pandemic.

3.4. Instruments for data collection

Validated questionnaires used in previous studies on the subject in different parts of the world including the studies by Unver et al. (2017) , Szopiński and Bachnik (2021) , Pham et al. (2019) , Segbenya et al. (2022) , and Fieger (2012) were used. The questionnaires involved a Likert scale of 1–5 to assess the various aspects of students’ experience in the online-learning classroom (i.e., internet access and cost, teaching performance and engagement, online classroom perceived quality, and acquisition of self-confidence in the online classroom) and their effect on students’ satisfaction, where ‘5’ was an opinion indicating that the student strongly agreed and ‘1’ was an indicator that the student strongly disagreed. Details of the variables and their items used for the study can be found in the Supplementary material . The questionnaire was made up of two sections; background information of respondent (sex, age, education level, frequency of participation in online leaning, and preference) and assessment of online learning. The assessment of frequency of participation in online leaning and preference for online or offline leaning was deducted from the work of Szopiński and Bachnik, (2021). The two items for assessing internet access and cost (IAC1 and IAC2) were deducted from the questionnaire used in the study by Segbenya et al. (2022) , while the four items used to measure online class perceived-quality (OCPQ1 – OCPQ4) were extracted from Pham et al. (2019) . The questionnaire on teaching performance and engagement was deducted from the study by Unver et al. (2017) (TPE2 and TPE3) and Fieger (2012) (TPE1 and TPE4), whereas all four items used to measure self-confidence (ASC1 – ASC4) were deducted from the study of Unver et al. (2017) . The questions on preference for online learning (POL1 – POL4) were deducted from the work of Segbenya et al. (2022) and overall students satisfaction (SS) from Alqurashi, (2019) and Fieger, (2012). The internal consistency of the questionnaire was checked and the CFA loadings of all variables had a significant value of p of <0.001 and reliability Cronbach’s alpha coefficient greater than the recommended threshold of 0.7 (except for internet access and cost-IAC, α  = 0.643). Since an AVE < 0.50 but >40 with an α value <0.6 is acceptable, all the variables in this study were valid and reliable for the dataset. In other words, the results indicated that the scales had satisfactory internal consistency and acceptable convergent validity.

3.5. Instrument validity/reliability

The questionnaire was subjected to review by the researcher’s colleagues and a pilot test among a few participants to ensure that any irrelevant material, contradictions, spelling errors, offensive language, and discrepancies were eliminated. This also ensured that ambiguity was eliminated and that sensitive questions were rephrased or avoided entirely. Moreover, the test of the reliability of the questionnaire by means of Cronbach’s coefficient of reliability indicated internal consistency ( Table 1 ).

Results of CFA loadings, reliability, and validity.

S.E., standard error; C.R., critical ratio; α , Cronbach’s alpha; AVE, average variance extracted. *** p  < 0.001. Abbreviations: OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; IAC, internet access and cost.

3.6. Data collection and analysis

The application of Google Docs in designing the online questionnaires made the data collation simpler since the total data collected was summarized and presented on a spreadsheet. The data was analyzed using the IBM® SPSS® software platform (version 26) and AMOS (version 26) software, where both descriptive and inferential statistics were conducted. Frequencies, means, and standard deviations were applied to describe the demographics of participants and examine their experience, preference, and satisfaction concerning online learning. Moreover, correlation analysis was applied to determine whether or not there was a significant relationship between the research variables, by comparing the means. In addition, a structural equation model (SEM) was used to analyze the students’ responses to examine the effect of students’ online-learning experience on their satisfaction, as well as the direct and mediation influence of students’ preferences on this relationship. This simulation was carried out by measuring, assessing, and calculating the constraints or the parameters, including exploratory factor analysis (EFA) with SPSS® and confirmatory factor analysis (CFA) with AMOS. In this process, the validity, reliability, and construct loadings were performed. The path coefficient, predictive accuracy (R2), effect size (f2), and predictive relevance (Q2) were also calculated. All statistically significant values were set at a significance level of p  ≤ 0.05.

3.6.1. Reliability and validity of measurement model

Validity describes the extent to which a measurement item truly measures what it is expected to measure, while reliability describes an instrument’s consistency ( Diamantopoulos and Temme, 2013 ; Lowry and Gaskin, 2014 ; Gaskin and Lim, 2016 ). Concerning the validity of the indicators, the researcher examined the paths’ weight and significance, linking each latent variable to its observed variables. The observed variables’ loadings should be significant ( p  < 0.05 or better), and the t-values are expected to be 1.96 in absolute terms. The reliabilities of the observed variables were assessed by examining the squared multiple correlations. A higher multiple-squared correlation value signifies the observed variable’s high reliability ( Boduszek et al., 2013 ).

3.6.2. Assessment of model fit

These indices employed in the model fit assessment included Chi-square (x 2 ), the normed fit index (NFI), the standardized root mean square residual (SRMR), the comparative fit index (CFI), the root mean error square of approximation (RMSEA), and the goodness of fit index (CFI) as earlier indicated ( Gaskin and Lim, 2016 ).

3.6.3. Structural equation model

The structural equation model (SEM) was employed since it allows simultaneous evaluation of model construct relationships. The SEM served as not just a predictive model with a column vector, y, containing p-dependent variables, but also explicitly formulated as a causal model.

3.6.4. Assessment of the structural path model

To evaluate the structural aspect of the model, the paths linking the different independent variables (students’ online-learning experience consisting of internet access and cost, teaching performance and engagement, perceived quality of online-classroom learning, and acquisition of self-confidence), mediating variable (students’ preference) and the dependent unobserved variable (students’ satisfaction) were examined to determine whether the hypothesized relationships (H1, H2, H3, and H4) were supported by the data. The parameter signs linking the unobserved variables were also examined to establish adequate support for the hypothesized relationships. Moreover, the weight and significance of the parameter estimate and the squared multiple correlations ( R 2 ) were estimated to know the level of variance.

4.1. Sociodemographic of respondents

Out of the 501 respondents, 296 (59.1%) were males. Approximately half of the participants (253/501, 50.5%) were between 18 and 28 years and 215 (42.9%) were between 29 and 39 years. The participants were well-distributed between the different levels of university education to prevent skewed data, as approximately 31% represented undergraduate, 46% master, and 23% Ph.D. students. Other details on the sociodemographic are presented in Table 2 .

Sociodemographic of respondents.

4.2. Frequency of participation and preference for online classes

All respondents had participated in online classes before and during the COVID-19 pandemic. While approximately 32% were likely to have an increase in online class participation, 30% rather anticipated a decrease in online classroom learning. Interestingly, about 39% of the respondents indicated that their participation frequency in online classes would likely not change ( Table 3 ). There is a profound variation of the online learning environment from the traditional in-person classroom situation regarding outcomes such as learner satisfaction, motivation, and interaction ( Bignoux and Sund, 2018 ). On the phase value of their experience in the online classes, approximately 80% (402/501 participants) indicated their preference for in-person learning as against online learning ( Table 3 ).

Frequency of participation and preference for online classes.

4.3. Students’ opinions on online learning

The questionnaire employed a five-level scale with ‘5’ as an opinion indicating that the student strongly agreed and ‘1’ as an indicator that the student strongly disagreed. Interpretation and criteria values were 4.50–5.00 indicating ‘strongly agreed’, 3.50–4.49 indicating ‘agreed’, 2.50–3.49 indicating ‘neutral’, 1.50–2.49 indicating ‘disagreed’, and 1.00–1.49 indicating ‘strongly disagreed’ ( Ruenphongphun et al., 2021 ) to assess the students’ opinion concerning their online learning experience. The variable with the lowest score was teaching performance and engagement-TPE ( M  = 2.59, SD = 0.97), followed by the acquisition of self-confidence-ASC ( M  = 2.70, SD = 0.99) and online classroom perceived quality-OCPQ ( M  = 2.89, SD = 1.06) as presented in Table 4 . The neutral mean response indicates that although the students do not agree that they had a good online learning experience, they also disagree that it was poor. Neutral mean response scores were also recorded for internet access and cost-IAC ( M  = 3.04, SD = 0.82), preference for online learning-POL ( M  = 3.24, SD = 0.90), and students’ satisfaction-SS ( M  = 3.26, SD = 1.17). The high standard deviations noticed indicate that the data are more spread out; more variable in students’ opinions concerning their online-learning experience and satisfaction ( McGrath et al., 2020 ). However, only about 20% (99/501) of the students indicated their preference for online-learning relative to 80% who preferred face-to-face learning ( Table 3 ). The abrupt introduction of online classes without prior orientation and training might have contributed to the low preference or acceptance rate among the students.

Correlation analysis.

* p  < 0.05; ** p  < 0.01. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; POL, preference for online learning; IAC, internet access and cost; SS, students’ satisfaction.

4.4. The relationship between students’ online-learning experience (IAC, OCPQ, TPE, ASC), preference for online learning (POL), and students’ satisfaction (SS)

To examine the relationship between students’ online-learning experience and their overall satisfaction (SS) with online classes during the pandemic, correlation analysis was carried out ( Table 4 ) using Pearson Moment Correlation (r). Students’ online-learning experience was assessed using their responses to OCPQ, ASC, TPE, IAC, and POL. Results showed that SS had a significant positive correlation with OCPQ ( r  = 0.267, p  = 0.01), ASC ( r  = 0.434, p  = 0.01), TPE ( r  = 0.407, p  = 0.01), and POL ( r  = 0.772, p  = 0.01) but a negative correlation with IAC ( r  = −0.275, p  = 0.01). Moreover, while POL positively correlated with OCPQ, ASC, and TPE, there was rather a negative correlation of POL with IAC ( Table 4 ). This means that students develop a better experience with the online classes once there is increased online class perceived quality, enhanced teaching performance and engagement, and acquisition of self-confidence, leading to overall satisfaction. On the other hand, internet access and cost negatively influence student satisfaction.

4.5. Exploratory factor analysis

SPSS was employed to perform exploratory factor analysis (EFA). The rotated component matrix results obtained from the EFA were examined to know how the measures of the various parameters being considered in the study (i.e., OCPQ, ASC, TPE, POL, and IAC) were loaded onto their suggested constructs. The Kaiser-Meyer-Olkin Measure (KMO) of Sampling Adequacy and Bartlett’s Test of Sphericity were checked to ascertain whether the samples were sufficient to carry out the survey analysis. The amount of variance explained by the factors was also measured. The study analyzed the overall job satisfaction construct with one item, hence, it was not included in the EFA.

4.5.1. Kaiser–Meyer–Olkin Bartlett’s test

The EFA analysis produced a Kaiser-Meyer-Olkin Measure of Sampling Adequacy value of 0.869 with a value of p of less than 0.001 ( Table 5 ). This indicates that the sample was sufficiently adequate for the study.

Determination of sample sufficiency.

4.5.2. Eigenvalues and variances of the study variables

Eigenvalues express the total variance that could be explained by a given principal component. The Eigenvalues again. Represent the sum of squared component loadings across every item for each component, which stands for the amount of variance in each item that can be explained by the principal component. Thus, eigenvectors represent a weight for each eigenvalue ( Bruin, 2006 ). The EFA results showed five components with a sum eigenvalue and variance explained of 13.98 and 77.65%, respectively ( Table 6 ).

Total variance explained.

Extraction Method: Principal Component Analysis.

4.5.3. Rotated component matrix

The rotated component matrix, also known as the loadings, contains estimates of the correlations between the variables and the estimated components and serves as the main output of principal components analysis ( Guilloteau et al., 2021 ). Further EFA analysis using the Rotated Component Matrix showed that all the factor loadings for the variables under study were greater than the suggested threshold of 0.50 ( Table 7 ). The factor loadings ranged from 0.691 to 0.911, and they loaded well under their predicted construct. The results suggest acceptability for the items employed to measure the various constructs.

EFA via the rotated component matrix.

OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; POL, preference for online learning; IAC, internet access and cost; SS, students’ satisfaction.

4.6. Confirmatory factor analysis

After the EFA had identified the structure of the relationship between the variables and shown the sufficiency and validity of the dataset, the confirmatory factor analysis (CFA) was also carried out with the AMOS software to provide further reliability and validity to the data set. The CFA represents a multivariate statistical procedure employed to assess how well-measured variables act for the number of constructs and further allows the researcher to test if the hypothesis of any given relationship between an observed variable and its underlying latent construct exists ( Li et al., 2020 ; Agegnehu et al., 2022 ).

4.6.1. Validity and reliability of the variables in the study

In this process, the CFA loadings ( β ), which are expected to be greater than the recommended threshold of 0.50, were examined. Moreover, the data reliability was assessed using Cronbach’s alpha ( α ) and composite reliability, which are acceptable at a recommended threshold of greater than 0.70. The average variance extracted (AVE) and discriminant validity was relied on to establish the validity of the dataset. The AVE is ascribed to be better if it is greater than 0.50. However, an AVE less than 0.50 but greater than 0.40 with composite reliability greater than 0.60 can be accepted ( Fornell and Larcker, 1981 ). The results showed AVE values greater than 0.50, except for ICC which had an AVE value of 0.477 but a composite reliability value of 0.645 ( Table 1 ). Moreover, the CFA loadings of all variables had a significant p -value <0.001 and reliability Cronbach’s alpha coefficient greater than the recommended threshold of 0.7 (except for IAC, α  = 0.643). Since an AVE < 0.50 but >40 with an α value <0.6 is acceptable, all the variables in this study are valid and reliable for the dataset. In other words, the results indicate that the scales had satisfactory internal consistency and acceptable convergent validity.

Discriminant validity, a subtype of construct validity was further carried out to show how well the variables measure the concept designed to measure in this study. The goal of discriminant validity evidence is to be able to discriminate between measures of dissimilar constructs ( Hubley, 2014 ). Thus, the analysis was done to confirm that although the variables are related, they are very much distinct from each other. The results showed discriminant validity values greater than their corresponding latent variable correlation coefficients ( Table 8 ).

Correlation and discriminant validity results.

*** p -value < 0.001; Bold values represent discriminant. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; POL, student experience on online learning; IAC, internet access and cost.

4.6.2. Examining the measurement models for the various hypothesized relationships

The study further assessed the measurement models based on the construct’s relationships to other constructs in the model to give confidence in the structural models during hypotheses testing. Therefore, the study performed CFA with AMOS. In the process, the model fit indices such as the Chi-square ( χ 2 ), which is dependent on the sample size, standardized root mean square residual (SRMR <0.06), relative Chi-square index ( χ 2 /df), comparative fit index (CFI > 0.95), root mean square error of approximation (RMSEA <0.06), and Bentler-Bonett normed fit-index (NFI > 0.95) were checked for acceptability. The relationship between the various parameters of students’ online-learning experience and students’ preference for online learning was examined. The students’ satisfaction variable was excluded from the CFA because it is not a latent variable. It was measured with only an item. The structure produced a model fit indices of Chi-square (CMIN) = 567.475, degree freedom (df) = 125, relative Chi-square index (CMIN/df) = 4.540, comparative fit index (CFI) = 0.927, standardized root mean square residual (SRMR) = 0.067, root mean square error of approximation (RMSEA) = 0.074. The results suggest that the study’s model fits the data set well and it is suitable for further analysis ( Figure 2 ).

CFA Measurement Model. Model fit indices: Chi-square (CMIN) = 567.475, degree freedom (df) = 125, relative Chi-square index (CMIN/df) = 4.540, comparative fit index (CFI) = 0.927, standardized root mean square residual (SRMR) = 0.067, root mean square error of approximation (RMSEA) = 0.074. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; POL, preference for online learning; IAC, internet access and cost.

4.7. Hypotheses testing

The data analysis was carried out using the structural equation model (SEM) in AMOS software version 26. The SEM technique was employed to analyze the main effect involving the influence of the four facets of students’ online-learning experience (OCPQ, ASC, TPE, IAC) on students’ satisfaction with online learning. SEM was again used to analyze the mediation effect of students’ preference for online learning (POL) on their overall satisfaction as indicated in the conceptual framework. In analyzing the mediating effect of SEM, the direct paths from the students’ online-learning experience and students’ satisfaction with online learning were critically considered. The analysis was developed into the synopsis described below.

4.7.1. The effect of students’ online-learning experience on students’ satisfaction

The effects of online-learning experience (online classroom perceived quality, acquisition of self-confidence, teaching performance and engagement, internet access and cost) on students’ satisfaction was determined. Thus, the hypothesized relationship, H1 (H1a, H1b, H1c, H1d) was first tested using SEM, and the results are shown in Table 9 . The structural model ( Figure 3 ) for the main effect provided a good fit, where Chi-square (CMIN) = 357.058, degree freedom (df) = 87, relative Chi-square index (CMIN/df) = 4.104, comparative fit index (CFI) = 0.940, standardized root mean square residual (SRMR) = 0.071, and root mean square error of approximation (RMSEA) = 0.079. On the direct path without a mediator, all the four sub-hypotheses (H1a, H1b, H1c, H1d) were supported, indicating that OCPQ, ASC, and TPE had a significant positive effect on students’ satisfaction while IAC had a significant negative effect on students’ satisfaction ( Table 9 ).

The effect of students’ online-learning experience on students’ satisfaction.

*** p -value < 0.001. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; IAC, internet access and cost; SS, students’ satisfaction.

Main effect model. Model fit indices: Chi-square (CMIN) = 357.058, degree freedom (df) = 87, relative Chi-square index (CMIN/df) = 4.104, comparative fit index (CFI) = 0.940, standardized root mean square residual (SRMR) = 0.071, root mean square error of approximation (RMSEA) = 0.079. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; IAC, internet access and cost.

4.7.2. Examining the structural mediation model

The three remaining components of the hypothesis were examined:

1. The effect of students’ online-learning experience on their preference for online learning, as highlighted in hypothesis H2 (H2a, H2b, H2c, H2d).

2. The effect of preference for online learning on students’ satisfaction, as highlighted in hypothesis H3.

3. Finally, the mediating roles of preference for online learning on the relationship between students’ online-learning experience and their satisfaction, as highlighted in hypothesis H4 (H4a, H4b, H4c, H4d).

The structural mediation effect as presented in Figure 4 was examined. SEM in Amos version 26 software was employed to estimate all the direct and indirect paths. The bootstrap method of 5,000 samples at 95% confidence intervals was utilized to establish the mediation effect. According to the rule of thumb for the bootstrapping method, if zero does not fall within the lower and upper bound confidence intervals, then the outcome of the result is significant ( Hadi et al., 2016 ). However, if zero falls within the lower and upper bound confidence intervals, then the outcome of the result is not significant.

Structural mediation model. Model fit indices: Chi-square (CMIN) = 930.132, degree freedom (df) = 289, relative Chi-square index (CMIN/df) = 3.218, comparative fit index (CFI) = 0.908, standardized root mean square residual (SRMR) = 0.061, root mean square error of approximation (RMSEA) = 0.075. OCPQ, online classroom perceived quality; ASC, acquisition of self-confidence; TPE, teaching performance and engagement; POL, preference for online learning; IAC, internet access and cost.

The fit statistics for the structural mediation model showed good-fit results with a Chi-square (CMIN) = 930.132, degree freedom (df) = 289, relative Chi-square index (CMIN/df) = 3.218, comparative fit index (CFI) = 0.908, standardized root mean square residual (SRMR) = 0.061, root mean square error of approximation (RMSEA) = 0.075.

The structural mediation path analysis also revealed that all the dimensions of students’ online learning experience had a statistically significant impact on their preference for online learning. The results imply that IAC, TPE, ASC, and OCPQ significantly influenced POL. Hence, H2 (H2a, H2b, H2c, and H2d) are supported. The results also showed that POL had a significant effect on SS; hence, support H3.

Furthermore, the indirect path from IAC to SS through POL had a standardized coefficient value of −0.165 ( p  < 0.025) with a 95% bias-corrected confidence interval (CI) of [−0.223, −0.086]. Since zero did not fall within the 95%CI, it means POL played a significant mediating role in the relationship between IAC and SS. Therefore, the results support H4a. Also, the indirect path coefficient from TPE to SS through POL was 0.259 ( p  < 0.008) with a 95%CI of [0.188, 0.362], supporting H4b. Again, the indirect path coefficient from ASC to SS through POL was 0.185 ( p  < 0.012) with a 95%CI of [0.117, 0.342], supporting H4c. Lastly, the results revealed a standardized indirect path of 0.109 ( p  < 0.006) with a 9%%CI of [0.062, 0.172] from OCPQ to SS through POL, supporting H4d ( Table 10 , Figure 4 ).

Results of structural mediation analysis.

5. Discussion and conclusion

Student satisfaction is a crucial measure of how well students are doing in their classes and is linked with student retention ( Kuo et al., 2013 ; Fleming et al., 2017 ) and loyalty to the school ( Devinder and Datta, 2003 ), therefore, educational institutions view student satisfaction as a valuable asset as students are more likely to talk about their experiences positively and return as alumni ( Parahoo et al., 2016 ). A number of studies have outlined the sources of factors that influence student satisfaction, including educational quality, technological features, curriculum and instruction, student characteristics, interaction in classes, learning styles, support services, and on rare occasions, demographic characteristics ( Yilmaz, 2017 ; She et al., 2021 ). In the phase of the pandemic, most universities, especially in developing countries, started online learning for the first time and had no earlier experience with such a mode of learning, therefore, they were confronted with challenges such as how to adequately engage the students and satisfy their needs in the virtual learning classrooms ( Faize and Nawaz, 2020 ). As a result, several studies have assessed the experience of students during online classes to better ascertain the factors that significantly influence students’ satisfaction and the general success of the online-learning system ( Imsa-ard, 2020 ; Gopal et al., 2021 ; Ho et al., 2021 ; Jiang et al., 2021 ; She et al., 2021 ; Kornpitack and Sawmong, 2022 ). In this study, the variables selected to assess the relationship between students’ online learning experience and their satisfaction were online classroom perceived quality, acquisition of self-confidence, teaching performance and engagement, internet access and cost, and preference for online-learning ( Fieger, 2012 ; Unver et al., 2017 ; Pham et al., 2019 ; Szopiński and Bachnik, 2021 ; Segbenya et al., 2022 ).

We found that teaching performance and engagement positively influence students’ satisfaction. This agrees with a recent study on student satisfaction during the COVID-19 pandemic that showed that there is a significant positive relationship between students’ engagement (interaction) and online learning satisfaction, as well as engagement and acquisition of academic self-efficacy ( She et al., 2021 ). Again, students’ satisfaction is related to their engagement and motivation ( Karaoğlan Yılmaz, 2022 ). Interaction in an online learning setting has been regarded as a critical factor that determines to the extent which students are satisfied with their online education ( Cidral et al., 2018 ). According to Kuo et al. (2014) , a high level of interaction with the instructor, other learners, or content leads to high satisfaction and thus represents high engagement in online learning ( Kuo et al., 2014 ). In addition, the quality of the instructor, prompt feedback, course design, and expectation of students positively impact student satisfaction and further, student satisfaction positively impacts students’ performance ( Gopal et al., 2021 ). Invariably, lack of engagement is associated with student dissatisfaction, as insufficient student-teacher interaction and untimely feedback and question-answering from instructors contribute to dissatisfaction, and are among the common challenges students encountered during the first week of online learning during the COVID-19 outbreak ( Li et al., 2021 ). Lack of interaction often leads to poor student engagement and lower student satisfaction ( Rahmatpour et al., 2021 ). Moreover, students who experienced instructors’ poor teaching performance or lack of preparation for courses were dissatisfied with their online learning experience ( Li et al., 2021 ). Quality teaching performance, which includes sufficient interaction in the classroom, adequate student engagement, elaborate course structure, and teacher awareness and facilitation positively influence students’ perceived online learning satisfaction during the pandemic of COVID-19 ( Baber, 2020 ). Therefore, interaction in online learning often translates to students’ engagement in their academic activities, a characteristic of better teaching performance, and positively affects students’ satisfaction ( Kim and Kim, 2021 ).

The perceived quality of online learning significantly influences students’ acceptance or preference for online learning and their overall satisfaction. A study reported that students who perceive a poor formal online-learning orientation tend to respond in higher proportion to problems during online learning, including rejection of online teaching. They also highly associate online learning with insufficient learning resources, untimely feedback and question-answering, poor arrangements and scheduling, and poor preparation of courses ( Li et al., 2021 ), thus, the perceived quality of the online-learning setup significantly positively affects students’ satisfaction with online learning ( Li et al., 2021 ). It is also identified that perceived course quality or ease of use of online learning technology positively influences students’ online learning satisfaction, while computer anxiety negatively shapes students’ satisfaction ( Sun et al., 2008 ). Similarly, students’ perceptions of online learning difficulty influenced their satisfaction during the Covid-19 transition to online education. Students’ satisfaction was negatively affected by perceived technical skill requirements, as it predicted difficulty in using the online learning system and thus, influenced the effective online learning experiences and satisfaction ( Conrad et al., 2022 ). Accordingly to Jiang et al. (2021) , perceived quality, ease of use, and usefulness of the online learning platform affect online learning satisfaction in higher education during the COVID-19 pandemic ( Jiang et al., 2021 ). Other studies report factors such as perceived quality, ease of use, the usefulness of online platforms, online learning acceptance or preference, online support service quality, computer self-efficacy, academic self-efficacy, and prior experience as significant influencers of students’ online learning satisfaction ( Lee, 2010 ; Goldstraw et al., 2016 ; Jiang et al., 2021 ).

This study found that while internet access and cost negatively influence the overall students’ satisfaction, the acquisition of self-confidence positively influences students’ satisfaction with online learning. This is not surprising since internet connection serves as a critical infrastructural component of e-learning or mobile learning approaches ( Delnoij et al., 2020 ; Korkmaz et al., 2022 ) but appears to be less accessible and more expensive in developing countries, including Kenya, where only about 35% of the population has access to the Internet ( World Bank, 2022 ). Other reports indicate that a decent internet connection, which is essential for many basic tasks in the COVID-19 era, is out of reach for 90% of people in low-and middle-income countries ( Okoth, 2022 ) and high cost of Internet access remains one of the main barriers to the use of information and communication technology services worldwide ( Barton, 2021 ). Recent studies that examined students’ experience with online learning during the COVID-19 pandemic report that internet connectivity problems, including network congestion, negatively affect student satisfaction ( Li et al., 2021 ; Segbenya et al., 2022 ) It is also documented that among the key barriers that prevent students from satisfactory online education are accessibility and affordably of Internet usage, in addition to administrative and technical issues, lack of academic and technical skills, interaction, motivation, time, and support for studies ( Muilenburg and Berge, 2005 ). Concerning the positive influence of self-confidence on student satisfaction, similar studies indicate that the acquisition of academic self-efficacy and confidence has a positive effect on students’ engagement within self-directed distance education, where students with high academic self-efficacy and confidence are more engaged in their online studies ( Jung and Lee, 2018 ) and are more likely to experience learning satisfaction ( Artino, 2007 ). Moreover, self-confidence and self-efficacy, which is understood as students’ belief in the capability to perform academically well during an online platform, has been reported to be the most predictive factor of students’ satisfaction ( Shen et al., 2013 ; Jan, 2015 ). Students’ satisfaction showed a moderate and positive correlation with self-confidence in both simulation-oriented pre-clinical practice and clinical practice among nursing students ( Oanh et al., 2021 ). Other studies have also reported a positive correlation between the levels of students’ self-confidence and their satisfaction, which also positively influences their performance ( Almeida et al., 2015 ; Farrés-Tarafa et al., 2021 ).

Given the actual situation of the COVID-19 outbreak that impedes traditional face-to-face teaching and learning, online learning serves as a first-line solution for teaching and learning. However, the online learning environment varies profoundly from the traditional classroom situation when it comes to learner satisfaction, motivation, and interaction ( Bignoux and Sund, 2018 ). In this study, approximately 80% of the participants indicated their preference for in-person learning as against online learning, and preference for online learning positively correlated with students’ satisfaction. The less preference could be due to the fact that although the universities were engaging in online classrooms, the issues of preparedness or readiness for online learning, designing, and effectiveness remain challenges to be solved, thus the less preference by students as confirmed by Imsa-ard (2020) . Interestingly, some of the challenges that caused the lack of preference for the online-learning included the high cost of internet access and the inability to afford quality devices ( Wangkiat, 2021 ). One possible way to increase online learning preference, acceptance, and satisfaction as demonstrated by Faize and Nawaz (2020) is to identify the problems faced by students during online learning, seek their suggestions for overcoming them and work on the students’ opinions with a team of instructors to modify existing instructional practices during an online class. This results in increased student satisfaction with online learning ( Faize and Nawaz, 2020 ). The abrupt transition to online learning has reportedly contributed to pervasive negative reactions among students ( Besser et al., 2022 ) and has even taken a toll on many students’ mental health ( Copeland et al., 2021 ). Considering these factors among other challenges, it is not surprising that online learning had a low preference rate among university students in Kenya.

5.1. Conclusion

According to a report by UNESCO in 2021, more than 220 million students in higher education were affected by the closure of universities in 2020 ( UNESCO, 2021 ). Several studies emphasize the pivotal role that student satisfaction plays in determining the success or failure of online education ( Rabin et al., 2019 ; Gopal et al., 2021 ). Thus, this study examined the effect of the online-learning experience of higher education students on their satisfaction with online learning in the setting of the COVID-19 pandemic in Kenya. The mediating role of students’ preference on the relationship between online-learning experience and students’ satisfaction was also examined. Regardless of the mass application of online learning in Kenya, approximately 80% of university students still prefer face-to-face classes to online classes. Overall, students indicated a neutral position for the online-leaning experience, implying that although they did not have a better online learning experience, it was at the same time not bad. There is a positive effect of teaching performance and engagement, acquisition of self-confidence, and online classroom perceived quality on students’ satisfaction. On the other hand, students’ satisfaction negatively correlates with internet access and cost. Moreover, students’ preference for online learning positively influences their satisfaction and mediates the relationship between students’ experience and their overall satisfaction. The finding of this study further shed light on the underlying factors that explain students’ online learning satisfaction during the COVID-19 pandemic. Therefore, it provides a guideline for universities and policymakers to make better decisions that enhance students online learning satisfaction and ultimately lead to students’ academic outcomes and achievement.

Limitations of the study include data derived from a relatively short time and a one-time administration of the survey instrument during the academic year. Therefore, the stability of the satisfaction factors over an entire academic year has not been validated. Since the data collection spanned a period of 6 months, the variation in time could also influence the outcome due to the dynamic nature of students’ online learning experience and satisfaction. Moreover, the results best represent the online learning experience and satisfaction in the selected universities in Nairobi and may not necessarily be generalized. Again, online survey research using social media to reach students has the possibility of introducing response bias into the data, making the replication of studies more difficult. Finally, although the investigators collected extensive demographic data on the responding students, there was no possibility of controlling for many of the student characteristics that might have influenced the results. This raises a more general limitation resulting from the ease with which survey instruments can be distributed in the electronic environment. This causes many students to suffer “survey fatigue” which can adversely impact response rates.

Data availability statement

Ethics statement.

Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.

Author contributions

XL and FO: conceptualization, software, visualization, writing—original draft, writing—review and editing. XL: funding acquisition and project administration. DO: conceptualization, writing—original draft, writing—review and editing. All authors contributed to the article and approved the submitted version.

This work was supported by the 2018 Jiangsu provincial key research project related to philosophy and social science in higher educational institutes: Research on Learning Outcomes of Engineering Undergraduates Studying in China from Countries along “One Belt & One Road” (Grant no. 2018SJZDI172), and the teaching reform and research project in Jiangsu University: Research on the Influences of Student-Faculty Interaction upon the Development of Faculty Who Teach International Students Studying in China (Grant no. 2017JGYB010).

Conflict of interest

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

Publisher’s note

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

Supplementary material

The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2023.1095073/full#supplementary-material

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