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• Published: 25 January 2021

Online education in the post-COVID era

• Barbara B. Lockee 1  

Nature Electronics volume  4 ,  pages 5–6 ( 2021 ) Cite this article

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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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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.

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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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Systematic review article, a systematic review of the effectiveness of online learning in higher education during the covid-19 pandemic period.

• 1 Department of Basic Education, Beihai Campus, Guilin University of Electronic Technology Beihai, Beihai, Guangxi, China
• 2 School of Sports and Arts, Harbin Sport University, Harbin, Heilongjiang, China
• 3 School of Music, Harbin Normal University, Harbin, Heilongjiang, China
• 4 School of General Education, Beihai Vocational College, Beihai, Guangxi, China
• 5 School of Economics and Management, Beihai Campus, Guilin University of Electronic Technology, Guilin, Guangxi, China

Background: The effectiveness of online learning in higher education during the COVID-19 pandemic period is a debated topic but a systematic review on this topic is absent.

Methods: The present study implemented a systematic review of 25 selected articles to comprehensively evaluate online learning effectiveness during the pandemic period and identify factors that influence such effectiveness.

Results: It was concluded that past studies failed to achieve a consensus over online learning effectiveness and research results are largely by how learning effectiveness was assessed, e.g., self-reported online learning effectiveness, longitudinal comparison, and RCT. Meanwhile, a set of factors that positively or negatively influence the effectiveness of online learning were identified, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience.

Discussion: Although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education and these challenges and difficulties are more prominent in developing countries. In addition, this review critically assesses limitations in past research, develops pedagogical implications, and proposes recommendations for future research.

1 Introduction

1.1 research background.

The COVID-19 pandemic first out broken in early 2020 has considerably shaped the higher education landscape globally. To restrain viral transmission, universities globally locked down, and teaching and learning activities were transferred to online platforms. Although online learning is a relatively mature learning model and is increasingly integrated into higher education, the sudden and unprepared transition to wholly online learning caused by the pandemic posed formidable challenges to higher education stakeholders, e.g., policymakers, instructors, and students, especially at the early stage of the pandemic ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Correspondingly, the effectiveness of online learning during the pandemic period is still questionable as online learning during this period has some unique characteristics, e.g., the lack of preparation, sudden and unprepared transition, the huge scale of implementation, and social distancing policies ( Sharma et al., 2020 ; Rahman, 2021 ; Tsang et al., 2021 ; Hollister et al., 2022 ; Zhang and Chen, 2023 ). This question is more prominent in developing or undeveloped countries because of insufficient Internet access, network problems, the lack of electronic devices, and poor network infrastructure ( Adnan and Anwar, 2020 ; Muthuprasad et al., 2021 ; Rahman, 2021 ; Chandrasiri and Weerakoon, 2022 ).

Learning effectiveness is a key consideration of education as it reflects the extent to which learning and teaching objectives are achieved and learners’ needs are satisfied ( Joy and Garcia, 2000 ; Swan, 2003 ). Online learning was generally proven to be effective within a higher education context ( Kebritchi et al., 2017 ) prior to the pandemic. ICTs have fundamentally shaped the process of learning as they allow learners to learn anywhere and anytime, interact with others efficiently and conveniently, and freely acquire a large volume of learning materials online ( Kebritchi et al., 2017 ; Choudhury and Pattnaik, 2020 ). Such benefits may be offset by the challenges brought about by the pandemic. A lot of empirical studies globally have investigated the effectiveness of online learning but there is currently a scarcity of a systematic review of these studies to comprehensively evaluate online learning effectiveness and identify factors that influence effectiveness.

At present, although the vast majority of countries have implemented opening policies to deal with the pandemic and higher education institutes have recovered offline teaching and learning, assessing the effectiveness of online learning during the pandemic period via a systematic review is still essential. First, it is necessary to summarize, learn from, and reflect on the lessons and experiences of online learning practices during the pandemic period to offer implications for future practices and research. Second, the review of online learning research carried out during the pandemic period is likely to generate interesting knowledge because of the unique research context. Third, higher education institutes still need a contingency plan for emergency online learning to deal with potential crises in the future, e.g., wars, pandemics, and natural disasters. A systematic review of research on the effectiveness of online learning during the pandemic period offers valuable knowledge for designing a contingency plan for the future.

1.2 Related concepts

1.2.1 online learning.

Online learning should not be simply understood as learning on the Internet or the integration of ICTs with learning because it is a systematic framework consisting of a set of pedagogies, technologies, implementations, and processes ( Kebritchi et al., 2017 ; Choudhury and Pattnaik, 2020). Choudhury and Pattnaik (2020; p.2) summarized prior definitions of online learning and provided a comprehensive and up-to-date definition, i.e., online learning refers to “ the transfer of knowledge and skills, in a well-designed course content that has established accreditations, through an electronic media like the Internet, Web 4.0, intranets and extranets .” Online learning differs from traditional learning because of not only technological differences, but also differences in social development and pedagogies ( Camargo et al., 2020 ). Online learning has also considerably shaped the patterns by which knowledge is stored, shared, and transferred, skills are practiced, as well as the way by which stakeholders (e.g., teachers and teachers) interact ( Desai et al., 2008 ; Anderson and Hajhashemi, 2013 ). In addition, online learning has altered educational objectives and learning requirements. Memorizing knowledge was traditionally viewed as vital to learning but it is now less important since required knowledge can be conveniently searched and acquired on the Internet while the reflection and application of knowledge becomes more important ( Gamage et al., 2023 ). Online learning also entails learners’ self-regulated learning ability more than traditional learning because the online learning environment inflicts less external regulation and provides more autonomy and flexibility ( Barnard-Brak et al., 2010 ; Wong et al., 2019 ). The above differences imply that traditional pedagogies may not apply to online learning.

There are a variety of online learning models according to the differences in learning methods, processes, outcomes, and the application of technologies ( Zeitoun, 2008 ). As ICTs can be used as either the foundation of learning or auxiliary means, online learning can be classified into assistant, blended, and wholly online models. Here, assistant online learning refers to the scenario where online learning technologies are used to supplement and support traditional learning; blended online learning refers to the integration/ mixture of online and offline methods, and; wholly online learning refers to the exclusive use of the Internet for learning ( Arkorful and Abaidoo, 2015 ). The present review focuses on wholly online learning because the review is interested in the COVID-19 pandemic context where learning activities are fully switched to online platforms.

1.2.2 Learning effectiveness

Learning effectiveness can be broadly defined as the extent to which learning and teaching objectives have been effectively and efficiently achieved via educational activities ( Swan, 2003 ) or the extent to which learners’ needs are satisfied by learning activities ( Joy and Garcia, 2000 ). It is a multi-dimensional construct because learning objectives and needs are always diversified ( Joy and Garcia, 2000 ; Swan, 2003 ). Assessing learning effectiveness is a key challenge in educational research and researchers generally use a set of subjective and objective indicators to assess learning effectiveness, e.g., examination scores, assignment performance, perceived effectiveness, student satisfaction, learning motivation, engagement in learning, and learning experience ( Rajaram and Collins, 2013 ; Noesgaard and Ørngreen, 2015 ). Prior research related to the effectiveness of online learning was diversified in terms of learning outcomes, e.g., satisfaction, perceived effectiveness, motivation, and learning engagement, and there is no consensus over which outcomes are valid indicators of learning effectiveness. The present study adopts a broad definition of learning effectiveness and considers various learning outcomes that are closely associated with learning objectives and needs.

1.3 Previous review research

Up to now, online learning during the COVID-19 pandemic period has attracted considerable attention from academia and there is a lot of related review research. Some review research analyzed the trends and major topics in related research. Pratama et al. (2020) tracked the trend of using online meeting applications in online learning during the pandemic period based on a systematic review of 12 articles. It was reported that the use of these applications kept a rising trend and this use helps promote learning and teaching processes. However, this review was descriptive and failed to identify problems related to these applications as well as the limitations of these applications. Zhang et al. (2022) implemented a bibliometric review to provide a holistic view of research on online learning in higher education during the COVID-19 pandemic period. They concluded that the majority of research focused on identifying the use of strategies and technologies, psychological impacts brought by the pandemic, and student perceptions. Meanwhile, collaborative learning, hands-on learning, discovery learning, and inquiry-based learning were the most frequently discussed instructional approaches. In addition, chemical and medical education were found to be the most investigated disciplines. This review hence offered a relatively comprehensive landscape of related research in the field. However, since it was a bibliometric review, it merely analyzed the superficial characteristics of past articles in the field without a detailed analysis of their research contributions. Bughrara et al. (2023) categorized the major research topics in the field of online medical education during the pandemic period via a scoping review. A total of 174 articles were included in the review and it was found there were seven major topics, including students’ mental health, stigma, student vaccination, use of telehealth, students’ physical health, online modifications and educational adaptations, and students’ attitudes and knowledge. Overall, the review comprehensively reveals major topics in the focused field.

Some scholars believed that online learning during the pandemic period has brought about a lot of problems while both students and teachers encounter many challenges. García-Morales et al. (2021) implemented a systematic review to identify the challenges encountered by higher education in an online learning scenario during the pandemic period. A total of seven studies were included and it was found that higher education suddenly transferred to online learning and a lot of technologies and platforms were used to support online learning. However, this transition was hasty and forced by the extreme situation. Thus, various stakeholders in learning and teaching (e.g., students, universities, and teachers) encountered difficulties in adapting to this sudden change. To deal with these challenges, universities need to utilize the potential of technologies, improve learning experience, and meet students’ expectations. The major limitation of García-Morales et al. (2021) review of the small-sized sample. Meanwhile, García-Morales et al. (2021) also failed to systematically categorize various types of challenges. Stojan et al. (2022) investigated the changes to medical education brought about by the shift to online learning in the COVID-19 pandemic context as well as the lessons and impacts of these changes via a systematic review. A total of 56 articles were included in the analysis, it was reported that small groups and didactics were the most prevalent instructional methods. Although learning engagement was always interactive, teachers majorly integrated technologies to amplify and replace, rather than transform learning. Based on this, they argued that the use of asynchronous and synchronous formats promoted online learning engagement and offered self-directed and flexible learning. The major limitation of this review is that the article is somewhat descriptive and lacks the crucial evaluation of problems of online learning.

Review research has also focused on the changes and impacts brought by online learning during the pandemic period. Camargo et al. (2020) implemented a meta-analysis on seven empirical studies regarding online learning methods during the pandemic period to evaluate feasible online learning platforms, effective online learning models, and the optimal duration of online lectures, as well as the perceptions of teachers and students in the online learning process. Overall, it was concluded that the shift from offline to online learning is feasible, and; effective online learning needs a well-trained and integrated team to identify students’ and teachers’ needs, timely respond, and support them via digital tools. In addition, the pandemic has brought more or less difficulties to online learning. An obvious limitation of this review is the overly small-sized sample ( N  = 7), which offers very limited information, but the review tries to answer too many questions (four questions). Grafton-Clarke et al. (2022) investigated the innovation/adaptations implemented, their impacts, and the reasons for their selections in the shift to online learning in medical education during the pandemic period via a systematic review of 55 articles. The major adaptations implemented include the rapid shift to the virtual space, pre-recorded videos or live streaming of surgical procedures, remote adaptations for clinical visits, and multidisciplinary ward rounds and team meetings. Major challenges encountered by students and teachers include the need for technical resources, faculty time, and devices, the shortage of standardized telemedicine curricula, and the lack of personal interactions. Based on this, they criticized the quality of online medical education. Tang (2023) explored the impact of the pandemic on primary, secondary, and tertiary education in the pandemic context via a systematic review of 41 articles. It was reported that the majority of these impacts are negative, e.g., learning loss among learners, assessment and experiential learning in the virtual environment, limitations in instructions, technology-related constraints, the lack of learning materials and resources, and deteriorated psychosocial well-being. These negative impacts are amplified by the unequal distribution of resources, unfair socioeconomic status, ethnicity, gender, physical conditions, and learning ability. Overall, this review comprehensively criticizes the problems brought about by online learning during the pandemic period.

Very little review research evaluated students’ responses to online learning during the pandemic period. For instance, Salas-Pilco et al. (2022) evaluated the engagement in online learning in Latin American higher education during the COVID-19 pandemic period via a systematic review of 23 studies. They considered three dimensions of engagement, including affective, cognitive, and behavioral engagement. They described the characteristics of learning engagement and proposed suggestions for enhancing engagement, including improving Internet connectivity, providing professional training, transforming higher education, ensuring quality, and offering emotional support. A key limitation of the review is that these authors focused on describing the characteristics of engagement without identifying factors that influence engagement.

A synthesis of previous review research offers some implications. First, although learning effectiveness is an important consideration in educational research, review research is scarce on this topic and hence there is a lack of comprehensive knowledge regarding the extent to which online learning is effective during the COVID-19 pandemic period. Second, according to past review research that summarized the major topics of related research, e.g., Bughrara et al. (2023) and Zhang et al. (2022) , the effectiveness of online learning is not a major topic in prior empirical research and hence the author of this article argues that this topic has not received due attention from researchers. Third, some review research has identified a lot of problems in online learning during the pandemic period, e.g., García-Morales et al. (2021) and Stojan et al. (2022) . Many of these problems are caused by the sudden and rapid shift to online learning as well as the unique context of the pandemic. These problems may undermine the effectiveness of online learning. However, the extent to which these problems influence online learning effectiveness is still under-investigated.

1.4 Purpose of the review research

The research is carried out based on a systematic review of past empirical research to answer the following two research questions:

Q1: To what extent online learning in higher education is effective during the COVID-19 pandemic period?

Q2: What factors shape the effectiveness of online learning in higher education during the COVID-19 pandemic period?

2 Research methodology

2.1 literature review as a research methodology.

Regardless of discipline, all academic research activities should be related to and based on existing knowledge. As a result, scholars must identify related research on the topic of interest, critically assess the quality and content of existing research, and synthesize available results ( Linnenluecke et al., 2020 ). However, this task is increasingly challenging for scholars because of the exponential growth of academic knowledge, which makes it difficult to be at the forefront and keep up with state-of-the-art research ( Snyder, 2019 ). Correspondingly, literature review, as a research methodology is more relevant than previously ( Snyder, 2019 ; Linnenluecke et al., 2020 ). A well-implemented review provides a solid foundation for facilitating theory development and advancing knowledge ( Webster and Watson, 2002 ). Here, a literature review is broadly defined as a more or less systematic way of collecting and synthesizing past studies ( Tranfield et al., 2003 ). It allows researchers to integrate perspectives and results from a lot of past research and is able to address research questions unanswered by a single study ( Snyder, 2019 ).

There are generally three types of literature review, including meta-analysis, bibliometric review, and systematic review ( Snyder, 2019 ). A meta-analysis refers to a statistical technique for integrating results from a large volume of empirical research (majorly quantitative research) to compare, identify, and evaluate patterns, relationships, agreements, and disagreements generated by research on the same topic ( Davis et al., 2014 ). This study does not adopt a meta-analysis for two reasons. First, the research on the effectiveness of online learning in the context of the COVID-19 pandemic was published since 2020 and currently, there is a limited volume of empirical evidence. If the study adopts a meta-analysis, the sample size will be small, resulting in limited statistical power. Second, as mentioned above, there are a variety of indicators, e.g., motivation, satisfaction, experience, test score, and perceived effectiveness ( Rajaram and Collins, 2013 ; Noesgaard and Ørngreen, 2015 ), that reflect different aspects of online learning effectiveness. The use of diversified effectiveness indicators increases the difficulty of carrying out meta-analysis.

A bibliometric review refers to the analysis of a large volume of empirical research in terms of publication characteristics (e.g., year, journal, and citation), theories, methods, research questions, countries, and authors ( Donthu et al., 2021 ) and it is useful in tracing the trend, distribution, relationship, and general patterns of research published in a focused topic ( Wallin, 2005 ). A bibliometric review does not fit the present study for two reasons. First, at present, there are less than 4 years of history of research on online learning effectiveness. Hence the volume of relevant research is limited and the public trend is currently unclear. Second, this study is interested in the inner content and results of articles published, rather than their external characteristics.

A systematic review is a method and process of critically identifying and appraising research in a specific field based on predefined inclusion and exclusion criteria to test a hypothesis, answer a research question, evaluate problems in past research, identify research gaps, and/or point out the avenue for future research ( Liberati et al., 2009 ; Moher et al., 2009 ). This type of review is particularly suitable to the present study as there are still a lot of unanswered questions regarding the effectiveness of online learning in the pandemic context, a need for indicating future research direction, a lack of summary of relevant research in this field, and a scarcity of critical appraisal of problems in past research.

Adopting a systematic review methodology brings multiple benefits to the present study. First, it is helpful for distinguishing what needs to be done from what has been done, identifying major contributions made by past research, finding out gaps in past research, avoiding fruitless research, and providing insights for future research in the focused field ( Linnenluecke et al., 2020 ). Second, it is also beneficial for finding out new research directions, needs for theory development, and potential solutions for limitations in past research ( Snyder, 2019 ). Third, this methodology helps scholars to efficiently gain an overview of valuable research results and theories generated by past research, which inspires their research design, ideas, and perspectives ( Callahan, 2014 ).

Commonly, a systematic review can be either author-centric or theme-centric ( Webster and Watson, 2002 ) and the present review is theme-centric. Specifically, an author-centric review focuses on works published by a certain author or a group of authors and summarizes the major contributions made by the author(s; ( Webster and Watson, 2002 ). This type of review is problematic in terms of its incompleteness of research conclusions in a specific field and descriptive nature ( Linnenluecke et al., 2020 ). A theme-centric review is more common where a researcher guides readers through reviewing themes, concepts, and interesting phenomena according to a certain logic ( Callahan, 2014 ). A theme in this review can be further structured into several related sub-themes and this type of review helps researchers to gain a comprehensive understanding of relevant academic knowledge ( Papaioannou et al., 2016 ).

2.2 Research procedures

This study follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline ( Liberati et al., 2009 ) to implement a systematic review. The guideline indicates four phases of performing a systematic review, including (1) identifying possible research, (2) abstract screening, (3) assessing full-text for eligibility, and (4) qualitatively synthesizing included research. Figure 1 provides a flowchart of the process and the number of articles excluded and included in each phase.

Figure 1 . PRISMA flowchart concerning the selection of articles.

This study uses multiple academic databases to identify possible research, e.g., Academic Search Complete, IGI Global, ACM Digital Library, Elsevier (SCOPUS), Emerald, IEEE Xplore, Web of Science, Science Direct, ProQuest, Wiley Online Library, Taylor and Francis, and EBSCO. Since the COVID-19 pandemic broke out in January 2020, this study limits the literature search to articles published from January 2020 to August 2023. During this period, online learning was highly prevalent in schools globally and a considerable volume of articles were published to investigate various aspects of online learning in this period. Keywords used for searching possible research include pandemic, COVID, SARS-CoV-2, 2019-nCoV, coronavirus, online learning, e-learning, electronic learning, higher education, tertiary education, universities, learning effectiveness, learning satisfaction, learning engagement, and learning motivation. Aside from searching from databases, this study also manually checks the reference lists of relevant articles and uses Google Scholar to find out other articles that have cited these articles.

2.3 Inclusion and exclusion criteria

Articles included in the review must meet the following criteria. First, articles have to be written in English and published on peer-reviewed journals. The academic language being English was chosen because it is in the Q zone of the specified search engines. Second, the research must be carried out in an online learning context. Third, the research must have collected and analyzed empirical data. Fourth, the research should be implemented in a higher education context and during the pandemic period. Fifth, the outcome variable must be factors related to learning effectiveness, and included studies must have reported the quantitative results for online learning effectiveness. The outcome variable should be measured by data collected from students, rather than other individuals (e.g., instructors). For instance, the research of Rahayu and Wirza (2020) used teacher perception as a measurement of online learning effectiveness and was hence excluded from the sample. According to the above criteria, a total of 25 articles were included in the review.

2.4 Data extraction and analysis

Content analysis is performed on included articles and an inductive approach is used to answer the two research questions. First, to understand the basic characteristics of the 25 articles/studies, the researcher summarizes their types, research designs, and samples and categorizes them into several groups. The researcher carefully reads the full-text of these articles and codes valuable pieces of content. In this process, an inductive approach is used, and key themes in these studies have been extracted and summarized. Second, the researcher further categorizes these studies into different groups according to their similarities and differences in research findings. In this way, these studies are broadly categorized into three groups, i.e., (1) ineffective (2) neutral, and (3) effective. Based on this, the research answers the research question and indicates the percentage of studies that evidenced online learning as effective in a COVID-19 pandemic context. The researcher also discusses how online learning is effective by analyzing the learning outcomes brought by online learning. Third, the researcher analyzes and compares the characteristics of the three groups of studies and extracts key themes that are relevant to the conditional effectiveness of online learning from these studies. Based on this, the researcher identifies factors that influence the effectiveness of online learning in a pandemic context. In this way, the two research questions have been adequately answered.

3 Research results and discussion

3.1 study characteristics.

Table 1 shows the statistics of the 25 studies while Table 2 shows a summary of these studies. Overall, these studies varied greatly in terms of research design, research subjects, contexts, measurements of learning effectiveness, and eventually research findings. Approximately half of the studies were published in 2021 and the number of studies reduced in 2022 and 2023, which may be attributed to the fact that universities gradually implemented opening-up policies after 2020. China received the largest number of studies ( N  = 5), followed by India ( N = 4) and the United States ( N  = 3). The sample sizes of the majority of studies (88.0%) ranged between 101 and 500. As this review excluded qualitative studies, all studies included adopted a purely quantitative design (88.0%) or a mixed design (12.0%). The majority of the studies were cross-sectional (72%) and a few studies (2%) were experimental.

Table 1 . Statistics of studies included in the review.

Table 2 . A summary of studies reviewed.

3.2 The effectiveness of online learning

Overall, the 25 studies generated mixed results regarding the effectiveness of online learning during the pandemic period. 9 (36%) studies reported online learning as effective; 13 (52%) studies reported online learning as ineffective, and the rest 3 (12%) studies produced neutral results. However, it should be noted that the results generated by these studies are not comparable as they used different approaches to evaluate the effectiveness of online learning. According to the approach of evaluating online learning effectiveness, these studies are categorized into four groups, including (1) Cross-sectional evaluation of online learning effectiveness without a comparison with offline learning; without a control group ( N  = 14; 56%), (2) Cross-sectional comparison of the effectiveness of online learning with offline learning; without control group (7; 28%), (3) Longitudinal comparison of the effectiveness of online learning with offline learning, without a control group ( N  = 2; 8%), and (4) Randomized Controlled Trial (RCT); with a control group ( N  = 2; 8%).

The first group of studies asked students to report the extent to which they perceived online learning as effective, they had achieved expected learning outcomes through online learning, or they were satisfied with online learning experience or outcomes, without a comparison with offline learning. Six out of 14 studies reported online learning as ineffective, including Adnan and Anwar (2020) , Hong et al. (2021) , Mok et al. (2021) , Baber (2022) , Chandrasiri and Weerakoon (2022) , and Lalduhawma et al. (2022) . Five out of 14 studies reported online learning as effective, including Almusharraf and Khahro (2020) , Sharma et al. (2020) , Mahyoob (2021) , Rahman (2021) , and Haningsih and Rohmi (2022) . In addition, 3 out of 14 studies reported neutral results, including Cranfield et al. (2021) , Tsang et al. (2021) , and Conrad et al. (2022) . It should be noted that this measurement approach is problematic in three aspects. First, researchers used various survey instruments to measure learning effectiveness without reaching a consensus over a widely accepted instrument. As a result, these studies measured different aspects of learning effectiveness and hence their results may be incomparable. Second, these studies relied on students’ self-reports to evaluate learning effectiveness, which is subjective and inaccurate. Third, even though students perceived online learning as effective, it does not imply that online learning is more effective than offline learning because of the absence of comparables.

The second group of studies asked students to compare online learning with offline learning to evaluate learning effectiveness. Interestingly, all 7 studies, including Alawamleh et al. (2020) , Almahasees et al. (2021) , Gonzalez-Ramirez et al. (2021) , Muthuprasad et al. (2021) , Selco and Habbak (2021) , Hollister et al. (2022) , and Zhang and Chen (2023) , reported that online learning was perceived by participants as less effective than offline learning. It should be noted that these results were specific to the COVID-19 pandemic context where strict social distancing policies were implemented. Consequently, these results should be interpreted as online learning during the school lockdown period was perceived by participants as less effective than offline learning during the pre-pandemic period. A key problem of the measurement of learning effectiveness in these studies is subjectivity, i.e., students’ self-reported online learning effectiveness relative to offline learning may be subjective and influenced by a lot of factors caused by the pandemic, e.g., negative emotions (e.g., fear, loneliness, and anxiety).

Only two studies implemented a longitudinal comparison of the effectiveness of online learning with offline learning, i.e., Chang et al. (2021) and Fyllos et al. (2021) . Interestingly, both studies reported that participants perceived online learning as more effective than offline learning, which is contradicted with the second group of studies. In the two studies, the same group of students participated in offline learning and online learning successively and rated the effectiveness of the two learning approaches, respectively. The two studies were implemented by time coincidence, i.e., researchers unexpectedly encountered the pandemic and subsequently, school lockdown when they were investigating learning effectiveness. Such time coincidence enabled them to compare the effectiveness of offline and online learning. However, this research design has three key problems. First, the content of learning in the online and offline learning periods was different and hence the evaluations of learning effectiveness of the two periods are not comparable. Second, self-reported learning effectiveness is subjective. Third, students are likely to obtain better examination scores in online examinations than in offline examinations because online examinations bring a lot of cheating behaviors and are less fair than offline examinations. As reported by Fyllos et al. (2021) , the examination score after online learning was significantly higher than after offline learning. Chang et al. (2021) reported that participants generally believed that offline examinations are fairer than online examinations.

Lastly, only two studies, i.e., Jiang et al. (2023) and Shirahmadi et al. (2023) , implemented an RCT design, which is more persuasive, objective, and accurate than the above-reviewed studies. Indeed, implementing an RCT to evaluate the effectiveness of online learning was a formidable challenge during the pandemic period because of viral transmission and social distancing policies. Both studies reported that online learning is more effective than offline learning during the pandemic period. However, it is questionable about the extent to which such results are affected by health/safety-related issues. It is reasonable to infer that online learning was perceived by students as safer than offline learning during the pandemic period and such perceptions may affect learning effectiveness.

Overall, it is difficult to conclude whether online learning is effective during the pandemic period. Nevertheless, it is possible to identify factors that shape the effectiveness of online learning, which is discussed in the next section.

3.3 Factors that shape online learning effectiveness

Infrastructure factors were reported as the most salient factors that determine online learning effectiveness. It seems that research from developed countries generated more positive results for online learning than research from less developed countries. This view was confirmed by the cross-country comparative study of Cranfield et al. (2021) . Indeed, online learning entails the support of ICT infrastructure, and hence ICT related factors, e.g., Internet connectivity, technical issues, network speed, accessibility of digital devices, and digital devices, considerably influence the effectiveness of online learning ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Prior review research, e.g., Tang (2023) also suggested that the unequal distribution of resources and unfair socioeconomic status intensified the problems brought about by online learning during the pandemic period. Salas-Pilco et al. (2022) recommended that improving Internet connectivity would increase students’ engagement in online learning during the pandemic period.

Adnan and Anwar (2020) study is one of the most cited works in the focused field. They reported that online learning is ineffective in Pakistan because of the problems of Internet access due to monetary and technical issues. The above problems hinder students from implementing online learning activities, making online learning ineffective. Likewise, Lalduhawma et al. (2022) research from India indicated that online learning is ineffective because of poor network interactivity, slow data speed, low data limits, and expensive costs of devices. As a result, online learning during the COVID-19 pandemic may have expanded the education gap between developed and developing countries because of developing countries’ infrastructure disadvantages. More attention to online learning infrastructure problems in developing countries is needed.

Instructional factors, e.g., course management and design, instructor characteristics, instructor-student interaction, assignments, and assessments were found to affect online learning effectiveness ( Sharma et al., 2020 ; Rahman, 2021 ; Tsang et al., 2021 ; Hollister et al., 2022 ; Zhang and Chen, 2023 ). Although these instructional factors have been well-documented as significant drivers of learning effectiveness in traditional learning literature, these factors in the pandemic period have some unique characteristics. Both students and teachers were not well prepared for wholly online instruction and learning in 2020 and hence they encountered a lot of problems in course management and design, learning interactions, assignments, and assessments ( Stojan et al., 2022 ; Tang, 2023 ). García-Morales et al. (2021) review also suggested that various stakeholders in learning and teaching encountered difficulties in adapting to the sudden, hasty, and forced transition of offline to online learning. Consequently, these instructional factors become salient in terms of affecting online learning effectiveness.

The negative role of the lack of social interaction caused by social distancing in affecting online learning effectiveness was highlighted by a lot of studies ( Almahasees et al., 2021 ; Baber, 2022 ; Conrad et al., 2022 ; Hollister et al., 2022 ). Baber (2022) argued that people give more importance to saving lives than socializing in the online environment and hence social interactions in learning are considerably reduced by social distancing norms. The negative impact of the lack of social interaction on online learning effectiveness is reflected in two aspects. First, according to a constructivist view, interaction is an indispensable element of learning because knowledge is actively constructed by learners in social interactions ( Woo and Reeves, 2007 ). Consequently, online learning effectiveness during the pandemic period is reduced by the lack of social interaction. Second, the lack of social interaction brings a lot of negative emotions, e.g., feelings of isolation, loneliness, anxiety, and depression ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). Such negative emotions undermine online learning effectiveness.

Negative emotions caused by the pandemic and school lockdown were also found to be detrimental to online learning effectiveness. In this context, it was reported that many students experience a lot of negative emotions, e.g., feelings of isolation, exhaustion, loneliness, and distraction ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). Such negative emotions, as mentioned above, reduce online learning effectiveness.

Several factors were also found to increase online learning effectiveness during the pandemic period, e.g., convenience and flexibility ( Hong et al., 2021 ; Muthuprasad et al., 2021 ; Selco and Habbak, 2021 ). Students with strong self-regulated learning abilities gain more benefits from convenience and flexibility in online learning ( Hong et al., 2021 ).

Overall, although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education. Meanwhile, the majority of students prefer offline learning to online learning. The above challenges and difficulties are more prominent in developing countries than in developed countries.

3.4 Pedagogical implications

The results generated by the systematic review offer a lot of pedagogical implications. First, online learning entails the support of ICT infrastructure, and infrastructure defects strongly undermine learning effectiveness ( García-Morales et al., 2021 ; Grafton-Clarke et al., 2022 ). Given the fact online learning is increasingly integrated into higher education ( Kebritchi et al., 2017 ) regardless of the presence of the pandemic, governments globally should increase the investment in learning-related ICT infrastructure in higher education institutes. Meanwhile, schools should consider students’ affordability of digital devices and network fees when implementing online learning activities. It is important to offer material support for those students with poor economic status. Infrastructure issues are more prominent in developing countries because of the lack of monetary resources and poor infrastructure base. Thus, international collaboration and aid are recommended to address these issues.

Second, since the lack of social interaction is a key factor that reduces online learning effectiveness, it is important to increase social interactions during the implementation of online learning activities. On the one hand, both students and instructors are encouraged to utilize network technologies to promote inter-individual interactions. On the other hand, the two parties are also encouraged to engage in offline interaction activities if the risk is acceptable.

Third, special attention should be paid to students’ emotions during the online learning process as online learning may bring a lot of negative emotions to students, which undermine learning effectiveness ( Alawamleh et al., 2020 ; Gonzalez-Ramirez et al., 2021 ; Selco and Habbak, 2021 ). In addition, higher education institutes should prepare a contingency plan for emergency online learning to deal with potential crises in the future, e.g., wars, pandemics, and natural disasters.

3.5 Limitations and suggestions for future research

There are several limitations in past research regarding online learning effectiveness during the pandemic period. The first is the lack of rigor in assessing learning effectiveness. Evidently, there is a scarcity of empirical research with an RCT design, which is considered to be accurate, objective, and rigorous in assessing pedagogical models ( Torgerson and Torgerson, 2001 ). The scarcity of ICT research leads to the difficulty in accurately assessing the effectiveness of online learning and comparing it with offline learning. Second, the widely accepted criteria for assessing learning effectiveness are absent, and past empirical studies used diversified procedures, techniques, instruments, and criteria for measuring online learning effectiveness, resulting in difficulty in comparing research results. Third, learning effectiveness is a multi-dimensional construct but its multidimensionality was largely ignored by past research. Therefore, it is difficult to evaluate which dimensions of learning effectiveness are promoted or undermined by online learning and it is also difficult to compare the results of different studies. Finally, there is very limited knowledge about the difference in online learning effectiveness between different subjects. It is likely that the subjects that depend on lab-based work (e.g., experimental physics, organic chemistry, and cell biology) are less appropriate for online learning than the subjects that depend on desk-based work (e.g., economics, psychology, and literature).

To deal with the above limitations, there are several recommendations for future research on online learning effectiveness. First, future research is encouraged to adopt an RCT design and collect a large-sized sample to objectively, rigorously, and accurately quantify the effectiveness of online learning. Second, scholars are also encouraged to develop a new framework to assess learning effectiveness comprehensively. This framework should cover multiple dimensions of learning effectiveness and have strong generalizability. Finally, it is recommended that future research could compare the effectiveness of online learning between different subjects.

4 Conclusion

This study carried out a systematic review of 25 empirical studies published between 2020 and 2023 to evaluate the effectiveness of online learning during the COVID-19 pandemic period. According to how online learning effectiveness was assessed, these 25 studies were categorized into four groups. The first group of studies employed a cross-sectional design and assessed online learning based on students’ perceptions without a control group. Less than half of these studies reported online learning as effective. The second group of studies also employed a cross-sectional design and asked students to compare the effectiveness of online learning with offline learning. All these studies reported that online learning is less effective than offline learning. The third group of studies employed a longitudinal design and compared the effectiveness of online learning with offline learning but without a control group and this group includes only 2 studies. It was reported that online learning is more effective than offline learning. The fourth group of studies employed an RCT design and this group includes only 2 studies. Both studies reported online learning as more effective than offline learning.

Overall, it is difficult to conclude whether online learning is effective during the pandemic period because of the diversified research contexts, methods, and approaches in past research. Nevertheless, the review identifies a set of factors that positively or negatively influence the effectiveness of online learning, including infrastructure factors, instructional factors, the lack of social interaction, negative emotions, flexibility, and convenience. Although it is debated over the effectiveness of online learning during the pandemic period, it is generally believed that the pandemic brings a lot of challenges and difficulties to higher education. Meanwhile, the majority of students prefer offline learning to online learning. In addition, developing countries face more challenges and difficulties in online learning because of monetary and infrastructure issues.

The findings of this review offer significant pedagogical implications for online learning in higher education institutes, including enhancing the development of ICT infrastructure, providing material support for students with poor economic status, enhancing social interactions, paying attention to students’ emotional status, and preparing a contingency plan of emergency online learning.

The review also identifies several limitations in past research regarding online learning effectiveness during the pandemic period, including the lack of rigor in assessing learning effectiveness, the absence of accepted criteria for assessing learning effectiveness, the neglect of the multidimensionality of learning effectiveness, and limited knowledge about the difference in online learning effectiveness between different subjects.

To deal with the above limitations, there are several recommendations for future research on online learning effectiveness. First, future research is encouraged to adopt an RCT design and collect a large-sized sample to objectively, rigorously, and accurately quantify the effectiveness of online learning. Second, scholars are also encouraged to develop a new framework to assess learning effectiveness comprehensively. This framework should cover multiple dimensions of learning effectiveness and have strong generalizability. Finally, it is recommended that future research could compare the effectiveness of online learning between different subjects. To fix these limitations in future research, recommendations are made.

It should be noted that this review is not free of problems. First, only studies that quantitatively measured online learning effectiveness were included in the review and hence a lot of other studies (e.g., qualitative studies) that investigated factors that influence online learning effectiveness were excluded, resulting in a relatively small-sized sample and incomplete synthesis of past research contributions. Second, since this review was qualitative, it was difficult to accurately quantify the level of online learning effectiveness.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.

Author contributions

WM: Writing – original draft, Writing – review & editing. LY: Writing – original draft, Writing – review & editing. CL: Writing – review & editing. NP: Writing – review & editing. XP: Writing – review & editing. YZ: Writing – review & editing.

The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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.

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Keywords: COVID-19 pandemic, higher education, online learning, learning effectiveness, systematic review

Citation: Meng W, Yu L, Liu C, Pan N, Pang X and Zhu Y (2024) A systematic review of the effectiveness of online learning in higher education during the COVID-19 pandemic period. Front. Educ . 8:1334153. doi: 10.3389/feduc.2023.1334153

Received: 06 November 2023; Accepted: 27 December 2023; Published: 17 January 2024.

Reviewed by:

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

*Correspondence: Lei Yu, [email protected]

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7 High-Impact, Evidence-Based Tips for Online Teaching

What do highly effective teachers do in online classrooms? We combed through dozens of studies to find the best research-backed ideas.

When online classes exploded in popularity a decade ago, the U.S. Department of Education embarked on an ambitious project : Researchers pored through more than a thousand studies to determine whether students in online classrooms do worse, as well, or better than those receiving face-to-face instruction. They discovered that on average, “students in online learning conditions performed modestly better than those receiving face-to-face instruction.”

But there was a significant caveat: It wasn’t the technology that mattered. In fact, many studies have found that technology actually hinders learning when deployed in a way that doesn't take advantage of the medium. All too often, for example, teachers would take a face-to-face lesson and replicate it online, a costly though understandable approach that rarely led to improvements. The key question for the researchers from the Dept. of Education was whether an online activity served as “a replacement for face-to-face instruction or as an enhancement of the face-to-face learning experience.”

“This finding suggests that the positive effects associated with blended learning should not be attributed to the medium,” the researchers wrote. Online teaching required specialized knowledge, an understanding of the strategies that would allow teachers to adapt technology to suit their pedagogical needs—not the other way around.

Yet the large-scale disruption caused by the pandemic forced millions of teachers to quickly adapt to online teaching, often with little training and preparation. “I feel like a first-year teacher again, only worse,” Justin Lopez-Cardoze, a seventh-grade science teacher told the Washington Post .

So how can teachers enhance the learning experience in online classrooms? We looked over all the research we've read about online learning to find seven high-impact, evidence-based strategies that every teacher should know.

1. Your Virtual Classroom is a Real Learning Space—Keep it Organized

“Students value strong course organization,” explain Swapna Kumar and her colleagues in a 2019 study . They point out that teachers who are new to online instruction are often too focused on content—converting their lectures, presentations, and worksheets into digital format—leaving course design as a secondary consideration.

While “novice instructors have subject-matter expertise, it’s the design that falls short,” Kumar points out, explaining that novice teachers often “don’t know how to organize their materials or set up a design that makes sense” to students.

When students see a well-organized virtual classroom, they’re more engaged, more confident, and more autonomous, says Sarah Schroeder, an associate professor at the University of Cincinnati. And students who encounter messy online learning environments actually project that judgment onto the teacher ; they conclude that the teacher is disorganized more generally.

Here are a few simple tips for organizing your virtual classroom:

• Have a single, dedicated hub where students can go every day to find their assignments, and other crucial announcements.
• Create and articulate the simplest communications plan you can. For example, it may be that students can reach you via text during working hours, and via email after school.
• Consider holding “learn your technology” days with your class to walk through common-use cases, like submitting work or signing on to synchronous lessons.
• Make an extra effort to be clear and concise in your directions, and consider making a short daily video summarizing the day’s objectives. When writing, avoid the dreaded “wall of text” and use numbered lists and short paragraphs with subheadings.
• Get rid of visual clutter. This includes hard-to-read fonts and unnecessary decorations or images.

2. Chunk Your Lessons Into Smaller, Digestible Pieces

In a 2010 study , researchers examined how well high school students learned from an online science curriculum and concluded that on average, online materials “require high mental effort” to process. “Working memory capacity is limited, and a learner can only deal with a few concepts simultaneously,” the researchers explain.

What would normally be a 30-minute activity in a face-to-face classroom should be much shorter in the virtual one. Instead of recording an entire lecture, consider creating several smaller ones, each covering a single key idea. The ideal duration for an instructional video, according to a 2014 study , is about 6 minutes, and researchers recorded steep drop-offs in attention after 9 minutes.

In order to give students additional time to process the material, alternate high- and low-intensity activities, and incorporate brain breaks regularly throughout the school day.

3. The Best Online Teachers Solicit Lots of Feedback

When you’re standing face-to-face with your students, you can usually tell when a lesson’s working. If students are riveted, their eyes light up and their brains are in overdrive. But in a virtual classroom, much of that information is lost.

That’s why the authors of a 2019 study which sought to identify the methods of the best online teachers say that you should regularly “gather student feedback on various aspects of...online courses” in order to identify “what was working or not.”

Unlike formative assessment, which focuses on how well students understand the material, it’s crucial that you also gauge how well students can access your virtual materials, according to the researchers. Most teachers and students are newbies in virtual classrooms, and serious communication and process-oriented issues can go undetected—and fester. Consider using student surveys administered via simple tools like Google Forms to ask questions such as: Are you having any technical problems? Are you able to quickly find and submit your work? Is this virtual classroom easy to navigate?

4. Annotate and Interject to Scaffold Learning

If you’re standing in your classroom and you want students to pay attention to something—perhaps a location on a map or information on a slide—you can use gestures to direct students’ attention. But that context can be hard to reproduce online.

To compensate, use simple annotations like arrows and text labels to provide “visual scaffolding and help direct the users' attention to those aspects that are important in learning materials and help guide learners' cognitive processes,” say the authors of a 2020 study . The researchers demonstrated that students who were shown maps with visual and text cues, like arrows and labels identifying key locations, scored 35 percent higher on a recall test than those exposed to maps with no cues.

Also, strategically interject questions into an instructional video at key points to check for understanding. Questions that prompt critical thinking like “Can you think of any exceptions to this rule?” or that probe for comprehension like “How do you determine momentum from measures of mass and velocity?” not only keep the lesson lively but promote deeper engagement with the material and allow you to assess learning, according to a 2018 study .

5. Frequent, Low-Stakes Quizzes are Easy to Do, and Highly Effective

Low- and no-stakes practice tests enhance retention of the material—and students who struggle the most benefit the most from weekly practice quizzes, according to a recent meta-analysis . While online quizzes don’t provide a greater benefit than paper ones, they can be automatically graded, saving hours of work.

You can use popular tools like Kahoot and Quizlet to create online quizzes that are not only fun, but also help students re-process and retain the material better. If you want to boost engagement even further, you can create a Jeopardy! board to gamify your quizzes.

6. Fight The Isolation of Remote Learning by Connecting With Your Students

You’re not just physically separated from your students. As classrooms move online, the psychological and emotional distance also increases, eroding the critical social context that is fertile soil for learning, according to a 2016 study . You’ll need to make special efforts to create a sense of community in your virtual classroom.

“To offset the isolating effects of an online class, teachers can strive to communicate more regularly and more informally with students,” writes Jason Dockter, a professor of English at Lincoln Land Community College in the study. The goal isn’t just to address academic issues, but to demonstrate “that the teacher is personally interested and invested in each student.”

John Thomas, an elementary school teacher, uses daily morning meetings , which can be done both synchronously and asynchronously, to check in with his students. Using Seesaw, he records a greeting that students can respond to and builds in “interactive, engaging activities designed to help our students learn more about themselves and their classmates”—such as sharing a favorite book or the family pet.

Beyond morning meetings, you can adapt many face-to-face activities to work in virtual classrooms:

• Use unstructured time to chat at the beginning of class.
• Try Zoom's "waiting room" feature to welcome kids to class one by one.
• Use breakout rooms to split students into small groups for show-and-tell, two truths and a lie, or other relationship-building exercises .
• At the end of the day, ask students to reflect on their learning with discussion prompts or a closing activity like appreciation, apology, or aha!
• Pose fun questions like “What’s your favorite movie?” in your all-class video tool, or on digital whiteboards like Jamboard or Padlet , and have students share out.

7. Take Care of Yourself

You’re not alone: teacher well-being has experienced a “steep decline” in recent months, with 71% of teachers reporting lower morale levels compared to pre-pandemic levels. As the adage goes, “You can’t serve from an empty cup.” If we want our students to succeed, we need to ensure that our teachers are taken care of. Not only is teacher stress contagious , resulting in higher stress levels for students, but it also passes through as poorer academic performance for students as well.

“In order for any of us to provide that safe, stable, and nurturing environment for the children that we serve, we have to practice self-care so that we can be available,” said Dr. Nadine Burke Harris, a pediatrician and California’s first surgeon general, in a recent interview with Edutopia. “Please make sure to put your own oxygen mask on and practice real care for yourself so that you can be there for the next generation.”

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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Three approaches to improve your online teaching

When designing online courses and teaching remotely, teachers need to select the framework that supports learning goals. Here, three academics break online learning techniques into their key parts

Antoni Badia

.css-76pyzs{margin-right:0.25rem;} ,, consuelo garcía, julio meneses, additional links.

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Many university teachers need help identifying the right conceptual framework for designing online courses and teaching in virtual learning environments (VLEs). A quick online search will suggest a multitude of pedagogic approaches and supposed innovative teaching methods, but many of these are not evidence-based. 

So, here we will summarise three research-based  online teaching approaches that teachers can use to guide their work in VLEs. We explain the focus, roles of teacher and students, the online learning environment and learning resources for each approach.

The content-acquisition approach

The main aim of this approach is to provide subject content to students. It is designed to cover all the essential concepts and principles of the subject matter. The content should be accessible, clear, concise, relevant, interactive, well explained and up to date to facilitate the students’ comprehensive understanding of the subject matter.

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Teacher role: The teacher is the primary source of knowledge. They are responsible for selecting and organising the course content, choosing the appropriate learning resources for students, designing the learning activities and assessing students’ learning outcomes.

Student role:  The student   is expected to acquire and learn the course content from the provided resources and complete the learning assignments. Each student’s acquired knowledge should accurately represent the course content. Most students’ learning time is devoted to self-paced learning.

Online learning environment: This should facilitate learners’ content acquisition. Thus, technology-based tools are mainly used to produce, provide access to and deliver learning resources and materials. The VLE will contain lesson plans, activities and grades and class recordings. The technology used needs to deliver live online classes, with teachers’ real-time explanations, to many students worldwide. A prototype example of the content acquisition approach can be seen in most  massive open online courses (Moocs).

Learning resources: Learning resources may be selected and designed to accurately represent the nature of the content and enhance students’ learning experience. Various kinds of resources may be used to represent and deliver the content, such as multimedia (video and audio tools), authoring tools, interactive resources (computer simulations, games, virtual labs), mobile resources and adaptive resources (intelligent tutoring systems).

The knowledge-building approach

The key feature of this approach is the knowledge-building processes through which students expand their individual understanding.

Teacher role: The teacher’s primary role is to guide, help and supervise students’ learning. The course design includes learning objectives, content, activities, scaffolding and support, and assessments. Course implementation involves supervision to ensure the correct application of learning skills and adequate task completion, correcting students’ misunderstandings, and monitoring and assessing the learning process and learning outcomes.

Student role: The student should actively engage in an individual knowledge-building process and activate high-level thinking processes. This is encouraged through enquiry-based assignments in which students must find solutions to project-based challenges or work through real-world or theoretical examples. Such assignments require a deep understanding of the content, challenging students to explore information, apply critical thinking , ask questions, design enquiries, interpret evidence, form explanations and communicate findings. Students must make connections, and apply and use their knowledge in authentic learning situations.

Online learning environment: Technology should help students to develop the best individual knowledge-building processes, learn content and complete activities. These environments integrate learning tools that support students’ learning and work on specific higher-order-thinking processes such as solving authentic problems. The  web-based inquiry science environment (WISE) provides a good example of an online learning environment of this kind.

Learning resources: Generally referred to as instructional scaffolding, resources combine content and learning guides, question prompts, model examples and conceptual, procedural, strategic and metacognitive supports to guide learning.

The collaborative learning approach

The critical feature of this approach is students’ collaboration in learning in virtual environments, usually using synchronous and written communication. 

Teacher role: The teacher aims to facilitate high-quality social participation among students in collaborative learning activities. To achieve this, the teacher should create a learning environment that promotes relationships of trust and mutual commitment and provide learning resources that guide students’ social participation and collaboration in groups and in the community. The teacher should encourage communication, solve student conflicts, control learning periods and monitor students’ learning pace. Content and activities, such as online debates and team presentations, should be designed to facilitate collaboration and learning among equals.

Student role: Students engage naturally and proactively in community participation and group work and collaborate with peers to complete learning assignments, solve problems and share knowledge and perspectives. They participate in discussion and group work, and use critical thinking and creativity .

Online learning environments: Digital tools should be used that facilitate group-work organisation, promote group communication and collaboration, support knowledge exchange, and elaborate on shared outcomes. One example of such an online environment is Moodle, particularly the chat, forum, wiki and glossary functions.

Learning resources: Participants are expected to take an active role, building on the initial selection of learning resources the teacher provides according to their learning needs. For example, a shared learning task for students may be solving a problem or tackling a learning challenge collaboratively. In that situation, the students should search for and select new information related to the specific issue or challenge of the learning assignment and share it with their peers.

Teachers may use a single teaching approach to design an online course, but it is more likely they will take features from all three and combine them to best fit the different elements of the course and its learning objectives.

Antoni Badia is director of the bachelor’s degree in primary education, and  Julio Meneses is director of learning and teaching analysis at the eLearning Innovation Center; both are at the Open University of Catalonia. Consuelo García is vice-rector for teaching and educational innovation at the Valencian International University.

If you would like advice and insight from academics and university staff delivered direct to your inbox each week, sign up for the Campus newsletter .

For more teaching and learning insight from the authors, see “ Emotions in response to teaching online: Exploring the factors influencing teachers in a fully online university ”, as published in Innovations in Education and Teaching International .  

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Should online educational platforms offer courses following a schedule or release them on demand?

by Marilyn Stone, American Marketing Association

Researchers from Carnegie Mellon University and University of Pennsylvania have published a new Journal of Marketing study that examines online educational platforms and the question of whether they should release content through a scheduled format that resembles a traditional university course or use an on-demand release strategy.

The study is titled "More Likely to Pay but Less Engaged: The Effects of Switching Online Courses from Scheduled to On-Demand Release on User Behavior" and is authored by Joy Lu, Eric T. Bradlow, and J. Wesley Hutchinson.

In 2011, the online education industry catered to around 300,000 consumers. In 2021, it served 220 million, thanks in part to increased enrollment during the COVID-19 pandemic. Traditional universities and institutions are increasingly adopting hybrid course formats. For example, the number of full-time online MBA students surpassed in-person MBA students for the first time in the 2020-21 academic year.

Today, online educational platforms like Coursera and edX offer a range of flexible course content, but these firms are faced with a tricky question: Should they release content through a scheduled format that resembles a traditional university course with a subset of lectures and quizzes available at the start of each week, or should they follow in the footsteps of Netflix and Hulu with an on-demand release strategy where all the material is immediately available upon registration?

This new article finds that the choice of format for content release not only impacts overall user engagement and firm revenue but also user performance and learning outcomes.

The researchers studied over 67,000 users taking an introductory marketing course on Coursera consisting of 32 short lecture videos and four quizzes. The study took advantage of a natural experiment policy change where the platform switched the course from a scheduled format to an on-demand release format while keeping the actual content the same.

The scheduled format closely resembled a traditional university course, with some of the study material available at the beginning of each week for four weeks. In the on-demand format, all four weeks of content was made available upon registration. All users could take the course for free or opt into paying for a completion certificate, either as a one-time fee in the scheduled format or a monthly subscription in the on-demand format.

More users, less engagement

The study's findings show that the switch to on-demand content doubled the percentage of paying users from 14% to 28%. Lu explains that "the on-demand format was successful in increasing short-term firm revenue by bringing in more paying users. On the downside, the switch resulted in significantly lower lecture completion rates and lower quiz performance."

The on-demand format also negatively impacted downstream platform engagement. The marketing course was promoted in a "Business Foundations" set with three other courses on operations, accounting, and finance.

"Compared to users in the scheduled format, those in the on-demand format ended up taking one or two fewer additional courses six months after the focal marketing course," says Bradlow.

Analysis of user activity reveals two new learning patterns:

• A subset (13%) of users in the on-demand format continued to return and take quizzes well beyond the recommended four-week course period. The greater flexibility in the on-demand content release and payment structure likely enabled these users to "stretch out" their consumption.
• The on-demand format increased the practice of binging—with user activity being clumped together (i.e., more binging) as compared to being evenly spaced out (i.e., less binging). In the scheduled format, binging was negatively related to course performance, which is consistent with the intuition that binging reflects procrastination or cramming. However, in the on-demand format, binging was positively related to performance, suggesting that on-demand users may binge as a form of strategic time management by setting aside time to consume in spurts.

Real-world implications

This study offers vital lessons for chief marketing officers in the online education space:

• The switch to the on-demand format attracted a set of users who were more likely to pay, but were less engaged in the course. On-demand content is potentially helpful at bringing in a new user segment or expanding the current user base, similar to universities offering concurrent hybrid MBAs that cater to busy students with full-time jobs. Managers must consider the trade-off between offering structure versus flexibility and may even consider offering different content release options simultaneously but at different price points by emphasizing their unique features.
• Platforms may need to adapt their content to account for users who binge on content and others who space it out over time. For example, firms can include more recaps or reviews to reduce frustration resulting from users forgetting content. It may even be a viable strategy to embrace the prevalence of binging among users by highlighting or designing sets of lectures that are "bingeable" versus more modular.
• Many online platforms offer episodic content that may be released in installments and thus need to make decisions regarding the content release format.

"Our study provides insights that help managers anticipate the potential consequences of such decisions," says Hutchinson. "On-demand content offers clear short-term benefits in terms of increased revenue but potentially long-term costs in terms of decreased engagement and new challenges in maintaining user engagement."

Journal information: Journal of Marketing

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Most Asian Americans say they have given to US charities, survey data finds

They are the only major U.S. racial or ethnic group in the country that is majority immigrant, Pew Research found — and now the organization says survey data shows that most Asian Americans say they have given to U.S. charities. Pew says Asian adults are more likely to say they have given to charity in the U.S. than in their Asian ancestral homeland, at 64% for U.S. charitable giving, and 20% giving to their Asian country of origin.

Pew said the organization wanted to better understand how family and personal connections in Asian American communities come into play with the connections they have to their ancestral homeland, and how philanthropic sentiment ties in.

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Immigrants made up 54% of all Asian Americans and 67% of Asian American adults in 2022, Pew Research found in an analysis released after surveying members of Asian American communities, and 54% of Asian adults said they have immediate family members who still live in their Asian country of origin.

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Pew found that among Asian adults who were born in the United States or immigrated to the U.S., most say they have given to a U.S. charitable organization. Within that demographic group, it is Filipino, Indian and Vietnamese adults in the U.S. who are more likely than other Asian-origin groups to say they have sent remittance money back home in the 12 months before they were surveyed, Pew found. The data showed that Chinese and Japanese adults were the least likely to say they had sent remittance money back home.

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Among Asian Americans who said they sent remittance money back home in the 12 months before the survey, the most common reason, at 63%, was to help family members with common daily expenses, with 50% reporting they sent money to help family with health care costs.

Pew's data found that in 2021 Asian Americans' countries of origin collectively received around $63 billion in remittance money sent from their relatives in the United States, and countries among the largest recipients of remittance money included the Philippines, China, Vietnam, South Korea, Japan and India. These countries are also the six most common origin countries for Asian Americans, Pew found. World Bank Data found that these six countries received about $55 billion of the $63 billion in remittance money, Pew reported.

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