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

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

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

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

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1 Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

2.1 Selecting and coding the data (studies)

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

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

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

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

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

2.2 Study group

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

2.3 Publication bias

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

2.4 Selecting the model

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

2.5 Heterogeneity

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

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

2.6 Interpreting the effect sizes

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

3 Findings and results

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4 Conclusions and discussion

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

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

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

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

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

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

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

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

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

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

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

*Studies included in meta-analysis

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

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Navigating the new normal: adapting online and distance learning in the post-pandemic era.

1. Introduction

1.1. background, 1.2. purpose of the review.

• Highlighting the multifaceted impact of the pandemic on education, including the disruptions caused by school closures and the subsequent shift to remote learning [ 1 ].
• Exploring innovative approaches and strategies employed by educators to ensure effective online teaching and learning experiences [ 2 , 4 ].
• Examining the role of technological solutions and platforms in facilitating remote education and their effectiveness in supporting teaching and learning processes [ 4 ].
• Investigating strategies for promoting student engagement and participation in virtual classrooms, considering the unique challenges and opportunities presented by online and distance learning [ 2 , 3 ].
• Evaluating the various assessment and evaluation methods employed in online education, considering their validity, reliability, and alignment with learning outcomes [ 4 ].
• Discussing the importance of supporting student well-being and academic success in the digital environment, addressing the social and emotional aspects of remote learning [ 3 ].
• Examining the professional development opportunities and resources available for educators to enhance their skills in online teaching and adapt to the changing educational landscape [ 4 ].
• Addressing equity and accessibility considerations in online and distance learning, developing strategies to ensure equitable opportunities for all learners and mitigate the digital divide [ 1 , 2 ].
• Identifying key lessons learned and best practices from the experiences of educators and students during the pandemic, providing insights for future educational practices [ 1 , 4 ].
• Discussing the potential for educational innovation and transformations in teaching and learning practices in the post-pandemic era, considering the lessons learned from the rapid transition to online and distance learning [ 4 ].

1.3. Significance of the Study

• To provide a comprehensive understanding of the impact of the pandemic on education. UNESCO (2020) reported that the widespread school closures caused by the pandemic disrupted traditional education practices and posed significant challenges for students, educators, and families [ 1 ]. As such, understanding the multifaceted impact of the pandemic is crucial for effective decision making and policy development.
• To highlight innovative approaches to online teaching and learning. Hodges et al. [ 4 ] emphasized the importance of instructional design principles and the use of educational technology tools in facilitating effective online education [ 4 ] by examining strategies employed by educators during the pandemic. This review paper aims to identify successful practices that can be applied in future online and blended learning environments.
• To explore the role of technology in supporting remote education. The rapid transition to online and distance learning has required the use of various technological solutions and platforms. With reference to this subject, Hodges et al. (2020) discussed the difference between emergency remote teaching and online learning, highlighting the importance of leveraging technology to create engaging and interactive virtual classrooms [ 4 ].
• To address equity and accessibility considerations. The pandemic has exacerbated existing inequities in access to education and technology. On this line, UNESCO (2020) emphasized the need to address equity issues and bridge the digital divide to ensure equitable opportunities for all learners. This review paper examines strategies and interventions aimed at promoting equitable access to online and distance learning.
• To provide insights for future educational practices by analyzing experiences, challenges, and successes encountered during the transition to online and distance learning. This review paper aims to provide valuable insights for educators, policymakers, and researchers. So, lessons learned from the pandemic can inform the development of effective educational policies, teacher training programs, and support systems for students.

1.4. Methodology of Search

2. impact of the covid-19 pandemic on education, 3. transitioning from traditional classrooms to online and distance learning, 4. challenges faced by educators during the lockdown period, 5. strategies for effective online teaching and learning, 6. technological solutions and platforms for remote education, 7. promoting student engagement and participation in the virtual classroom, 8. assessments and evaluation methods in online education, 9. supporting student well-being and academic success in the digital environment, 10. professional development for educators in online teaching, 11. addressing equity and accessibility in online and distance learning, 12. lessons learned and best practices for future educational practices, 13. innovations and transformations in education post-pandemic, 14. policy implications and recommendations for effective online education, 15. ethical considerations in online and distance learning, 16. innovations and practical applications in post-pandemic educational strategies.

• Impact Analysis Tools: Develop analytical tools to quantify the educational disruptions caused by the pandemic, focusing on metrics like attendance, engagement, and performance shifts due to remote learning.
• Online Pedagogy Workshops: Create workshops for educators to share and learn innovative online teaching strategies, focusing on interactivity, student-centered learning, and curriculum adaptation for virtual environments.
• Tech-Integration Frameworks: Develop frameworks for integrating and evaluating the effectiveness of various technological solutions in remote education, including LMS, interactive tools, and AI-based learning supports.
• Engagement-Boosting Platforms: Create platforms or tools that specifically target student engagement in virtual classrooms, incorporating gamification, interactive content, and real-time feedback mechanisms.
• Assessment Methodology Guides: Develop guidelines or toolkits for educators to design and implement valid and reliable online assessments aligned with learning outcomes.
• Well-being Monitoring Systems: Implement systems to monitor and support student well-being in digital learning environments, incorporating mental health resources and social-emotional learning components.
• Professional Development Portals: Develop online portals offering continuous professional development opportunities for educators, focusing on upskilling in digital pedagogy, content creation, and adaptive learning technologies.
• Equity and Accessibility Strategies: Formulate and implement strategies to ensure equitable access to online and distance learning, addressing the digital divide through resource distribution, adaptive technologies, and inclusive curriculum design.
• Best Practices Repository: Create a repository of best practices and lessons learned from the pandemic’s educational challenges, serving as a resource for future educational planning and crisis management.
• Post-Pandemic Educational Innovation Labs: Establish innovation labs to explore and pilot new teaching and learning practices in the post-pandemic era, emphasizing the integration of traditional and digital pedagogies.

17. Conclusions: Navigating the Path Forward in Online Education

Author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest.

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Share and Cite

Sato, S.N.; Condes Moreno, E.; Rubio-Zarapuz, A.; Dalamitros, A.A.; Yañez-Sepulveda, R.; Tornero-Aguilera, J.F.; Clemente-Suárez, V.J. Navigating the New Normal: Adapting Online and Distance Learning in the Post-Pandemic Era. Educ. Sci. 2024 , 14 , 19. https://doi.org/10.3390/educsci14010019

Sato SN, Condes Moreno E, Rubio-Zarapuz A, Dalamitros AA, Yañez-Sepulveda R, Tornero-Aguilera JF, Clemente-Suárez VJ. Navigating the New Normal: Adapting Online and Distance Learning in the Post-Pandemic Era. Education Sciences . 2024; 14(1):19. https://doi.org/10.3390/educsci14010019

Sato, Simone Nomie, Emilia Condes Moreno, Alejandro Rubio-Zarapuz, Athanasios A. Dalamitros, Rodrigo Yañez-Sepulveda, Jose Francisco Tornero-Aguilera, and Vicente Javier Clemente-Suárez. 2024. "Navigating the New Normal: Adapting Online and Distance Learning in the Post-Pandemic Era" Education Sciences 14, no. 1: 19. https://doi.org/10.3390/educsci14010019

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MINI REVIEW article

Distance learning in higher education during covid-19.

• 1 Department of Pedagogy of Higher Education, Kazan (Volga Region) Federal University, Kazan, Russia
• 2 Department of Jurisprudence, Bauman Moscow State Technical University, Moscow, Russia
• 3 Department of English for Professional Communication, Financial University under the Government of the Russian Federation, Moscow, Russia
• 4 Department of Foreign Languages, RUDN University, Moscow, Russia
• 5 Department of Medical and Social Assessment, Emergency, and Ambulatory Therapy, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

COVID-19’s pandemic has hastened the expansion of online learning across all levels of education. Countries have pushed to expand their use of distant education and make it mandatory in view of the danger of being unable to resume face-to-face education. The most frequently reported disadvantages are technological challenges and the resulting inability to open the system. Prior to the pandemic, interest in distance learning was burgeoning, as it was a unique style of instruction. The mini-review aims to ascertain students’ attitudes about distant learning during COVID-19. To accomplish the objective, articles were retrieved from the ERIC database. We utilize the search phrases “Distance learning” AND “University” AND “COVID.” We compiled a list of 139 articles. We chose papers with “full text” and “peer reviewed only” sections. Following the exclusion, 58 articles persisted. Then, using content analysis, publications relating to students’ perspectives on distance learning were identified. There were 27 articles in the final list. Students’ perspectives on distant education are classified into four categories: perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning. In all studies, due of pandemic constraints, online data gathering methods were selected. Surveys and questionnaires were utilized as data collection tools. When students are asked to compare face-to-face and online learning techniques, they assert that online learning has the potential to compensate for any limitations caused by pandemic conditions. Students’ perspectives and degrees of satisfaction range widely, from good to negative. Distance learning is advantageous since it allows for learning at any time and from any location. Distance education benefits both accomplishment and learning. Staying at home is safer and less stressful for students during pandemics. Distance education contributes to a variety of physical and psychological health concerns, including fear, anxiety, stress, and attention problems. Many schools lack enough infrastructure as a result of the pandemic’s rapid transition to online schooling. Future researchers can study what kind of online education methods could be used to eliminate student concerns.

Introduction

The pandemic of COVID-19 has accelerated the spread of online learning at all stages of education, from kindergarten to higher education. Prior to the epidemic, several colleges offered online education. However, as a result of the epidemic, several governments discontinued face-to-face schooling in favor of compulsory distance education.

The COVID-19 problem had a detrimental effect on the world’s educational system. As a result, educational institutions around the world developed a new technique for delivering instructional programs ( Graham et al., 2020 ; Akhmadieva et al., 2021 ; Gaba et al., 2021 ; Insorio and Macandog, 2022 ; Tal et al., 2022 ). Distance education has been the sole choice in the majority of countries throughout this period, and these countries have sought to increase their use of distance education and make it mandatory in light of the risk of not being able to restart face-to-face schooling ( Falode et al., 2020 ; Gonçalves et al., 2020 ; Tugun et al., 2020 ; Altun et al., 2021 ; Valeeva and Kalimullin, 2021 ; Zagkos et al., 2022 ).

What Is Distance Learning

Britannica defines distance learning as “form of education in which the main elements include physical separation of teachers and students during instruction and the use of various technologies to facilitate student-teacher and student-student communication” ( Simonson and Berg, 2016 ). The subject of distant learning has been studied extensively in the fields of pedagogics and psychology for quite some time ( Palatovska et al., 2021 ).

The primary distinction is that early in the history of distant education, the majority of interactions between professors and students were asynchronous. With the advent of the Internet, synchronous work prospects expanded to include anything from chat rooms to videoconferencing services. Additionally, asynchronous material exchange was substantially relocated to digital settings and communication channels ( Virtič et al., 2021 ).

Distance learning is a fundamentally different way to communication as well as a different learning framework. An instructor may not meet with pupils in live broadcasts at all in distance learning, but merely follow them in a chat if required ( Bozkurt and Sharma, 2020 ). Audio podcasts, films, numerous simulators, and online quizzes are just a few of the technological tools available for distance learning. The major aspect of distance learning, on the other hand, is the detailed tracking of a student’s performance, which helps to develop his or her own trajectory. While online learning attempts to replicate classroom learning methods, distant learning employs a computer game format, with new levels available only after the previous ones have been completed ( Bakhov et al., 2021 ).

In recent years, increased attention has been placed on eLearning in educational institutions because to the numerous benefits that have been discovered via study. These advantages include the absence of physical and temporal limits, the ease of accessing material and scheduling flexibility, as well as the cost-effectiveness of the solution. A number of other studies have demonstrated that eLearning is beneficial to both student gains and student performance. However, in order to achieve the optimum results from eLearning, students must be actively participating in the learning process — a notion that is commonly referred to as active learning — throughout the whole process ( Aldossary, 2021 ; Altun et al., 2021 ).

The most commonly mentioned negatives include technological difficulties and the inability to open the system as a result, low teaching quality, inability to teach applicable disciplines, and a lack of courses, contact, communication, and internet ( Altun et al., 2021 ). Also, misuse of technology, adaptation of successful technology-based training to effective teaching methods, and bad practices in managing the assessment and evaluation process of learning are all downsides of distance learning ( Debeş, 2021 ).

Distance Learning in a Pandemic Context

The epidemic forced schools, colleges, and institutions throughout the world to close their doors so that students might practice social isolation ( Toquero, 2020 ). Prior to the pandemic, demand for distance learning was nascent, as it was a novel mode of education, the benefits and quality of which were difficult to judge due to a dearth of statistics. But, in 2020, humanity faced a coronavirus pandemic, which accelerated the shift to distant learning to the point that it became the only viable mode of education and communication ( Viktoria and Aida, 2020 ). Due to the advancements in digital technology, educators and lecturers have been obliged to use E-learning platforms ( Benadla and Hadji, 2021 ).

In remote education settings for higher education, activities are often divided into synchronous course sessions and asynchronous activities and tasks. In synchronous courses, learners participate in interactive and targeted experiences that help them develop a fundamental grasp of technology-enhanced education, course design, and successful online instruction. Asynchronous activities and tasks, on the other hand, include tests, group work assignments, group discussion, feedback, and projects. Additionally, asynchronous activities and tasks are carried out via interactive video-based activities, facilitator meetings, live webinars, and keynote speakers ( Debeş, 2021 ).

According to Lamanauskas and Makarskaitė-Petkevičienė (2021) , ICT should be attractive for learners. Additionally, student satisfaction with ODL has a statistically significant effect on their future choices for online learning ( Virtič et al., 2021 ). According to Avsheniuk et al. (2021) , the majority of research is undertaken to categorize students’ views and attitudes about online learning, and studies examining students’ perspectives of online learning during the COVID-19 epidemic are uncommon and few. There is presently a dearth of research on the impact on students when schools are forced to close abruptly and indefinitely and transition to online learning communities ( Unger and Meiran, 2020 ). So that, the mini-review is aimed to examining the students’ views on using distance learning during COVID-19.

In order to perform the aim, the articles were searched through ERIC database. We use “Distance learning” AND “University” AND “COVID” as search terms. We obtained 139 articles. We selected “full text” and “Peer reviewed only” articles. After the exclusion, 58 articles endured. Then content analyses were used to determine articles related to students’ voices about distance learning. In the final list, there were 27 articles ( Table 1 ).

Table 1. Countries and data collection tools.

In the study, a qualitative approach and content analyses were preferred. Firstly, the findings related to students’ attitudes and opinions on distance learning were determined. The research team read selected sections independently. Researchers have come to a consensus on the themes of perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning. It was decided which study would be included in which theme/s. Finally, the findings were synthesized under themes.

Only 3 studies ( Lassoued et al., 2020 ; Viktoria and Aida, 2020 ; Todri et al., 2021 ) were conducted to cover more than one country. Other studies include only one country. Surveys and questionnaires were mostly used as measurement tools in the study. Due to pandemic restrictions, online data collection approaches were preferred in the data collection process.

Students’ views on distance learning are grouped under four themes. These themes are perception and attitudes, advantages of distance learning, disadvantages of distance learning, and challenges for distance learning.

Perception and Attitudes Toward Distance Learning

Students’ attitudes toward distance learning differ according to the studies. In some studies ( Mathew and Chung, 2020 ; Avsheniuk et al., 2021 ), it is stated that especially the students’ attitudes are positive, while in some studies ( Bozavlı, 2021 ; Yurdal et al., 2021 ) it is clearly stated that their attitudes are negative. In addition, there are also studies ( Akcil and Bastas, 2021 ) that indicate that students’ attitudes are at a moderate level. The transition to distance learning has been a source of anxiety for some students ( Unger and Meiran, 2020 ).

When the students’ satisfaction levels are analyzed, it is obvious from the research ( Gonçalves et al., 2020 ; Avsheniuk et al., 2021 ; Bakhov et al., 2021 ; Glebov et al., 2021 ; Todri et al., 2021 ) that the students’ satisfaction levels are high. In some studies, it is pronounced that the general satisfaction level of the participants is moderate ( Viktoria and Aida, 2020 ; Aldossary, 2021 ; Didenko et al., 2021 ) and low ( Taşkaya, 2021 ).

When students compare face-to-face and online learning methods, they state that online learning has opportunities to compensate for their deficiencies due to the pandemic conditions ( Abrosimova, 2020 ) and but they prefer face-to-face learning ( Gonçalves et al., 2020 ; Kaisar and Chowdhury, 2020 ; Bakhov et al., 2021 ). Distance learning is not sufficiently motivating ( Altun et al., 2021 ; Bozavlı, 2021 ), effective ( Beltekin and Kuyulu, 2020 ; Bozavlı, 2021 ), and does not have a contribution to students’ knowledge ( Taşkaya, 2021 ). Distance education cannot be used in place of face-to-face instruction ( Aldossary, 2021 ; Altun et al., 2021 ).

Advantages of Distance Learning

It is mostly cited advantages that distance learning has a positive effect on achievement and learning ( Gonçalves et al., 2020 ; Lin and Gao, 2020 ; Aldossary, 2021 ; Altun et al., 2021 ; Şahin, 2021 ). In addition, in distance learning, students can have more resources and reuse resources such as re-watching video ( Önöral and Kurtulmus-Yilmaz, 2020 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Martha et al., 2021 ).

Distance learning for the reason any time and everywhere learning ( Adnan and Anwar, 2020 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Todri et al., 2021 ). There is no need to spend money on transportation to and from the institution ( Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Nenakhova, 2021 ). Also, staying at home is safe during pandemics and less stressful for students ( Lamanauskas and Makarskaitė-Petkevičienė, 2021 ).

Challenges and Disadvantages of Distance Learning

Distance learning cannot guarantee effective learning, the persistence of learning, or success ( Altun et al., 2021 ; Benadla and Hadji, 2021 ). Students state that they have more works, tasks, and study loads in the distance learning process ( Mathew and Chung, 2020 ; Bakhov et al., 2021 ; Didenko et al., 2021 ; Nenakhova, 2021 ). Group working and socialization difficulties are experienced in distance learning ( Adnan and Anwar, 2020 ; Bozavlı, 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ). The absence of communication and face-to-face interaction is seen a disadvantage ( Didenko et al., 2021 ; Nenakhova, 2021 ).

It is difficult to keep attention on the computer screen for a long time, so distance-learning negatively affects concentration ( Bakhov et al., 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ). In addition, distance education prompts some physical and psychological health problems ( Kaisar and Chowdhury, 2020 ; Taşkaya, 2021 ).

Devices and internet connection, technical problems are mainly stated as challenges for distance learning ( Abrosimova, 2020 ; Adnan and Anwar, 2020 ; Mathew and Chung, 2020 ; Bakhov et al., 2021 ; Benadla and Hadji, 2021 ; Didenko et al., 2021 ; Lamanauskas and Makarskaitė-Petkevičienė, 2021 ; Nenakhova, 2021 ; Taşkaya, 2021 ; Şahin, 2021 ). In addition, some students have difficulties in finding a quiet and suitable environment where they can follow distance education courses ( Taşkaya, 2021 ). It is a disadvantage that students have not the knowledge and skills to use the technological tools used in distance education ( Lassoued et al., 2020 ; Bakhov et al., 2021 ; Didenko et al., 2021 ).

The purpose of this study is to ascertain university students’ perceptions about distant education during COVID-19. The study’s findings are intended to give context for developers of distant curriculum and higher education institutions.

According to Toquero (2020) , academic institutions have an increased need to enhance their curricula, and the incorporation of innovative teaching methods and tactics should be a priority. COVID-19’s lockout has shown the reality of higher education’s current state: Progressive universities operating in the twenty-first century did not appear to be prepared to implement digital teaching and learning tools; existing online learning platforms were not universal solutions; teaching staff were not prepared to teach remotely; their understanding of online teaching was sometimes limited to sending handbooks, slides, sample tasks, and assignments to students via email and setting deadlines for submission of completed tasks ( Didenko et al., 2021 ).

It is a key factor that student satisfaction to identify the influencers that emerged in online higher education settings ( Parahoo et al., 2016 ). Also, there was a significant positive relationship between online learning, social presence and satisfaction with online courses ( Stankovska et al., 2021 ). According to the findings, the attitudes and satisfaction levels of the students differ according to the studies and vary in a wide range from positive to negative attitudes.

According to the study’s findings, students responded that while online learning is beneficial for compensating for deficiencies during the pandemic, they would prefer face-to-face education in the future. This is a significant outcome for institutions. It is not desirable for all students to take their courses entirely online. According to Samat et al. (2020) , the one-size-fits-all approach to ODL implementation is inapplicable since it not only impedes the flow of information delivery inside the virtual classroom, but it also has an impact on psychological well-being because users are prone to become disturbed.

In distance learning, students can have more resources and reuse resources such as re-watching videos. So, distance learning has a positive effect on achievement and learning. Alghamdi (2021) stated that over the last two decades, research on the influence of technology on students’ academic success has revealed a range of good and negative impacts and relationships, as well as zero effects and relationship.

The result also shows that distance education prompts some physical and psychological health problems. Due to the difficulty of maintaining focus on a computer screen for an extended period of time, remote education has a detrimental effect on concentration. There is some evidence that students are fearful of online learning in compared to more traditional, or in-person, in-class learning environments, as well as media representations of emergencies ( Müller-Seitz and Macpherson, 2014 ).

Unsatisfactory equipment and internet connection, technical difficulties, and a lack of expertise about remote learning technology are frequently cited as distance learning issues. Due to the pandemic’s quick move to online education, many schools have an insufficient infrastructure. Infrastructure deficiency is more evident in fields that require laboratory work such as engineering ( Andrzej, 2020 ) and medicine ( Yurdal et al., 2021 ).

Conclusion and Recommendation

To sum up, students’ opinions and levels of satisfaction vary significantly, ranging from positive to negative. Distance learning for the reason any time and everywhere learning. Distance learning has a positive effect on achievement and learning. Staying at home is safe during pandemics and less stressful for students. Distance education prompts some physical and psychological health problems such as fear, anxiety, stress, and losing concentration. Due to the pandemic’s quick move to online education, many schools have an insufficient infrastructure. Future researchers can investigate what distance education models can be that will eliminate the complaints of students. Students’ positive attitudes and levels of satisfaction with their distant education programs have an impact on their ability to profit from the program. Consequently, schools wishing to implement distant education should begin by developing a structure, content, and pedagogical approach that would improve the satisfaction of their students. According to the findings of the study, there is no universally applicable magic formula since student satisfaction differs depending on the country, course content, and external factors.

Author Contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work, and approved it for publication.

This manuscript has been supported by the Kazan Federal University Strategic Academic Leadership Program.

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 : ICT, distance learning, COVID-19, higher education, online learning

Citation: Masalimova AR, Khvatova MA, Chikileva LS, Zvyagintseva EP, Stepanova VV and Melnik MV (2022) Distance Learning in Higher Education During Covid-19. Front. Educ. 7:822958. doi: 10.3389/feduc.2022.822958

Received: 26 November 2021; Accepted: 14 February 2022; Published: 03 March 2022.

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Copyright © 2022 Masalimova, Khvatova, Chikileva, Zvyagintseva, Stepanova and Melnik. 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: Alfiya R. Masalimova, [email protected]

† ORCID: Alfiya R. Masalimova, orcid.org/0000-0003-3711-2527 ; Maria A. Khvatova, orcid.org/0000-0002-2156-8805 ; Lyudmila S. Chikileva, orcid.org/0000-0002-4737-9041 ; Elena P. Zvyagintseva, orcid.org/0000-0001-7078-0805 ; Valentina V. Stepanova, orcid.org/0000-0003-0495-0962 ; Mariya V. Melnik, orcid.org/0000-0001-8800-4628

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This study is a comparative analysis of online distance learning and traditional in-person education at King Saud University in Saudi Arabia, with a focus on understanding how different educational modalities affect student achievement. The justification for this study lies in the rapid shift towards online learning, especially highlighted by the educational changes during the COVID-19 pandemic. By analyzing the final test scores of freshman students in five core courses over the 2020 (in-person) and 2021 (online) academic years, the research provides empirical insights into the efficacy of online versus traditional education. Initial observations suggested that students in online settings scored lower in most courses. However, after adjusting for variables like gender, class size, and admission scores using multiple linear regression, a more nuanced picture emerged. Three courses showed better performance in the 2021 online cohort, one favored the 2020 in-person group, and one was unaffected by the teaching format. The study emphasizes the crucial need for a nuanced, data-driven strategy in integrating online learning within higher education systems. It brings to light the fact that the success of educational methodologies is highly contingent on specific contextual factors. This finding advocates for educational administrators and policymakers to exercise careful and informed judgment when adopting online learning modalities. It encourages them to thoroughly evaluate how different subjects and instructional approaches might interact with online formats, considering the variable effects these might have on learning outcomes. This approach ensures that decisions about implementing online education are made with a comprehensive understanding of its diverse and context-specific impacts, aiming to optimize educational effectiveness and student success.

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Introduction.

The year 2020 marked an extraordinary period, characterized by the global disruption caused by the COVID-19 pandemic. Governments and institutions worldwide had to adapt to unforeseen challenges across various domains, including health, economy, and education. In response, many educational institutions quickly transitioned to distance teaching (also known as e-learning, online learning, or virtual classrooms) to ensure continued access to education for their students. However, despite this rapid and widespread shift to online learning, a comprehensive examination of its effects on student achievement in comparison to traditional in-person instruction remains largely unexplored.

In research examining student outcomes in the context of online learning, the prevailing trend is the consistent observation that online learners often achieve less favorable results when compared to their peers in traditional classroom settings (e.g., Fischer et al., 2020 ; Bettinger et al., 2017 ; Edvardsson and Oskarsson, 2008 ). However, it is important to note that a significant portion of research on online learning has primarily focused on its potential impact (Kuhfeld et al., 2020 ; Azevedo et al., 2020 ; Di Pietro et al., 2020 ) or explored various perspectives (Aucejo et al., 2020 ; Radha et al., 2020 ) concerning distance education. These studies have often omitted a comprehensive and nuanced examination of its concrete academic consequences, particularly in terms of test scores and grades.

Given the dearth of research on the academic impact of online learning, especially in light of Covid-19 in the educational arena, the present study aims to address that gap by assessing the effectiveness of distance learning compared to in-person teaching in five required freshmen-level courses at King Saud University, Saudi Arabia. To accomplish this objective, the current study compared the final exam results of 8297 freshman students who were enrolled in the five courses in person in 2020 to their 8425 first-year counterparts who has taken the same courses at the same institution in 2021 but in an online format.

The final test results of the five courses (i.e., University Skills 101, Entrepreneurship 101, Computer Skills 101, Computer Skills 101, and Fitness and Health Culture 101) were examined, accounting for potential confounding factors such as gender, class size and admission scores, which have been cited in past research to be correlated with student achievement (e.g., Meinck and Brese, 2019 ; Jepsen, 2015 ) Additionally, as the preparatory year at King Saud University is divided into five tracks—health, nursing, science, business, and humanity, the study classified students based on their respective disciplines.

Motivation for the study

The rapid expansion of distance learning in higher education, particularly highlighted during the recent COVID-19 pandemic (Volk et al., 2020 ; Bettinger et al., 2017 ), underscores the need for alternative educational approaches during crises. Such disruptions can catalyze innovation and the adoption of distance learning as a contingency plan (Christensen et al., 2015 ). King Saud University, like many institutions worldwide, faced the challenge of transitioning abruptly to online learning in response to the pandemic.

E-learning has gained prominence in higher education due to technological advancements, offering institutions a competitive edge (Valverde-Berrocoso et al., 2020 ). Especially during conditions like the COVID-19 pandemic, electronic communication was utilized across the globe as a feasible means to overcome barriers and enhance interactions (Bozkurt, 2019 ).

Distance learning, characterized by flexibility, became crucial when traditional in-person classes are hindered by unforeseen circumstance such as the ones posed by COVID-19 (Arkorful and Abaidoo, 2015 ). Scholars argue that it allows students to learn at their own pace, often referred to as self-directed learning (Hiemstra, 1994 ) or self-education (Gadamer, 2001 ). Additional advantages include accessibility, cost-effectiveness, and flexibility (Sadeghi, 2019 ).

However, distance learning is not immune to its own set of challenges. Technical impediments, encompassing network issues, device limitations, and communication hiccups, represent formidable hurdles (Sadeghi, 2019 ). Furthermore, concerns about potential distractions in the online learning environment, fueled by the ubiquity of the internet and social media, have surfaced (Hall et al., 2020 ; Ravizza et al., 2017 ). The absence of traditional face-to-face interactions among students and between students and instructors is also viewed as a potential drawback (Sadeghi, 2019 ).

Given the evolving understanding of the pros and cons of distance learning, this study aims to contribute to the existing literature by assessing the effectiveness of distance learning, specifically in terms of student achievement, as compared to in-person classroom learning at King Saud University, one of Saudi Arabia’s largest higher education institutions.

Academic achievement: in-person vs online learning

The primary driving force behind the rapid integration of technology in education has been its emphasis on student performance (Lai and Bower, 2019 ). Over the past decade, numerous studies have undertaken comparisons of student academic achievement in online and in-person settings (e.g., Bettinger et al., 2017 ; Fischer et al., 2020 ; Iglesias-Pradas et al., 2021 ). This section offers a concise review of the disparities in academic achievement between college students engaged in in-person and online learning, as identified in existing research.

A number of studies point to the superiority of traditional in-person education over online learning in terms of academic outcomes. For example, Fischer et al. ( 2020 ) conducted a comprehensive study involving 72,000 university students across 433 subjects, revealing that online students tend to achieve slightly lower academic results than their in-class counterparts. Similarly, Bettinger et al. ( 2017 ) found that students at for-profit online universities generally underperformed when compared to their in-person peers. Supporting this trend, Figlio et al. ( 2013 ) indicated that in-person instruction consistently produced better results, particularly among specific subgroups like males, lower-performing students, and Hispanic learners. Additionally, Kaupp’s ( 2012 ) research in California community colleges demonstrated that online students faced lower completion and success rates compared to their traditional in-person counterparts (Fig. 1 ).

The figure compared student achievement in the final tests in the five courses by year, using independent-samples t-tests; the results show a statistically-significant drop in test scores from 2020 (in person) to 2021 (online) for all courses except CT_101.

In contrast, other studies present evidence of online students outperforming their in-person peers. For example, Iglesias-Pradas et al. ( 2021 ) conducted a comparative analysis of 43 bachelor courses at Telecommunication Engineering College in Malaysia, revealing that online students achieved higher academic outcomes than their in-person counterparts. Similarly, during the COVID-19 pandemic, Gonzalez et al. ( 2020 ) found that students engaged in online learning performed better than those who had previously taken the same subjects in traditional in-class settings.

Expanding on this topic, several studies have reported mixed results when comparing the academic performance of online and in-person students, with various student and instructor factors emerging as influential variables. Chesser et al. ( 2020 ) noted that student traits such as conscientiousness, agreeableness, and extraversion play a substantial role in academic achievement, regardless of the learning environment—be it traditional in-person classrooms or online settings. Furthermore, Cacault et al. ( 2021 ) discovered that online students with higher academic proficiency tend to outperform those with lower academic capabilities, suggesting that differences in students’ academic abilities may impact their performance. In contrast, Bergstrand and Savage ( 2013 ) found that online classes received lower overall ratings and exhibited a less respectful learning environment when compared to in-person instruction. Nevertheless, they also observed that the teaching efficiency of both in-class and online courses varied significantly depending on the instructors’ backgrounds and approaches. These findings underscore the multifaceted nature of the online vs. in-person learning debate, highlighting the need for a nuanced understanding of the factors at play.

Theoretical framework

Constructivism is a well-established learning theory that places learners at the forefront of their educational experience, emphasizing their active role in constructing knowledge through interactions with their environment (Duffy and Jonassen, 2009 ). According to constructivist principles, learners build their understanding by assimilating new information into their existing cognitive frameworks (Vygotsky, 1978 ). This theory highlights the importance of context, active engagement, and the social nature of learning (Dewey, 1938 ). Constructivist approaches often involve hands-on activities, problem-solving tasks, and opportunities for collaborative exploration (Brooks and Brooks, 1999 ).

In the realm of education, subject-specific pedagogy emerges as a vital perspective that acknowledges the distinctive nature of different academic disciplines (Shulman, 1986 ). It suggests that teaching methods should be tailored to the specific characteristics of each subject, recognizing that subjects like mathematics, literature, or science require different approaches to facilitate effective learning (Shulman, 1987 ). Subject-specific pedagogy emphasizes that the methods of instruction should mirror the ways experts in a particular field think, reason, and engage with their subject matter (Cochran-Smith and Zeichner, 2005 ).

When applying these principles to the design of instruction for online and in-person learning environments, the significance of adapting methods becomes even more pronounced. Online learning often requires unique approaches due to its reliance on technology, asynchronous interactions, and potential for reduced social presence (Anderson, 2003 ). In-person learning, on the other hand, benefits from face-to-face interactions and immediate feedback (Allen and Seaman, 2016 ). Here, the interplay of constructivism and subject-specific pedagogy becomes evident.

Online learning. In an online environment, constructivist principles can be upheld by creating interactive online activities that promote exploration, reflection, and collaborative learning (Salmon, 2000 ). Discussion forums, virtual labs, and multimedia presentations can provide opportunities for students to actively engage with the subject matter (Harasim, 2017 ). By integrating subject-specific pedagogy, educators can design online content that mirrors the discipline’s methodologies while leveraging technology for authentic experiences (Koehler and Mishra, 2009 ). For instance, an online history course might incorporate virtual museum tours, primary source analysis, and collaborative timeline projects.

In-person learning. In a traditional brick-and-mortar classroom setting, constructivist methods can be implemented through group activities, problem-solving tasks, and in-depth discussions that encourage active participation (Jonassen et al., 2003 ). Subject-specific pedagogy complements this by shaping instructional methods to align with the inherent characteristics of the subject (Hattie, 2009). For instance, in a physics class, hands-on experiments and real-world applications can bring theoretical concepts to life (Hake, 1998 ).

In sum, the fusion of constructivism and subject-specific pedagogy offers a versatile approach to instructional design that adapts to different learning environments (Garrison, 2011 ). By incorporating the principles of both theories, educators can tailor their methods to suit the unique demands of online and in-person learning, ultimately providing students with engaging and effective learning experiences that align with the nature of the subject matter and the mode of instruction.

Course description

The Self-Development Skills Department at King Saud University (KSU) offers five mandatory freshman-level courses. These courses aim to foster advanced thinking skills and cultivate scientific research abilities in students. They do so by imparting essential skills, identifying higher-level thinking patterns, and facilitating hands-on experience in scientific research. The design of these classes is centered around aiding students’ smooth transition into university life. Brief descriptions of these courses are as follows:

University Skills 101 (CI 101) is a three-hour credit course designed to nurture essential academic, communication, and personal skills among all preparatory year students at King Saud University. The primary goal of this course is to equip students with the practical abilities they need to excel in their academic pursuits and navigate their university lives effectively. CI 101 comprises 12 sessions and is an integral part of the curriculum for all incoming freshmen, ensuring a standardized foundation for skill development.

Fitness and Health 101 (FAJB 101) is a one-hour credit course. FAJB 101 focuses on the aspects of self-development skills in terms of health and physical, and the skills related to personal health, nutrition, sports, preventive, psychological, reproductive, and first aid. This course aims to motivate students’ learning process through entertainment, sports activities, and physical exercises to maintain their health. This course is required for all incoming freshmen students at King Saud University.

Entrepreneurship 101 (ENT 101) is a one-hour- credit course. ENT 101 aims to develop students’ skills related to entrepreneurship. The course provides students with knowledge and skills to generate and transform ideas and innovations into practical commercial projects in business settings. The entrepreneurship course consists of 14 sessions and is taught only to students in the business track.

Computer Skills 101 (CT 101) is a three-hour credit course. This provides students with the basic computer skills, e.g., components, operating systems, applications, and communication backup. The course explores data visualization, introductory level of modern programming with algorithms and information security. CT 101 course is taught for all tracks except those in the human track.

Computer Skills 102 (CT 102) is a three-hour credit course. It provides IT skills to the students to utilize computers with high efficiency, develop students’ research and scientific skills, and increase capability to design basic educational software. CT 102 course focuses on operating systems such as Microsoft Office. This course is only taught for students in the human track.

Structure and activities

These courses ranged from one to three hours. A one-hour credit means that students must take an hour of the class each week during the academic semester. The same arrangement would apply to two and three credit-hour courses. The types of activities in each course are shown in Table 1 .

At King Saud University, each semester spans 15 weeks in duration. The total number of semester hours allocated to each course serves as an indicator of its significance within the broader context of the academic program, including the diverse tracks available to students. Throughout the two years under study (i.e., 2020 and 2021), course placements (fall or spring), course content, and the organizational structure remained consistent and uniform.

Participants

The study’s data comes from test scores of a cohort of 16,722 first-year college students enrolled at King Saud University in Saudi Arabia over the span of two academic years: 2020 and 2021. Among these students, 8297 were engaged in traditional, in-person learning in 2020, while 8425 had transitioned to online instruction for the same courses in 2021 due to the Covid-19 pandemic. In 2020, the student population consisted of 51.5% females and 48.5% males. However, in 2021, there was a reversal in these proportions, with female students accounting for 48.5% and male students comprising 51.5% of the total participants.

Regarding student enrollment in the five courses, Table 2 provides a detailed breakdown by average class size, admission scores, and the number of students enrolled in the courses during the two years covered by this study. While the total number of students in each course remained relatively consistent across the two years, there were noticeable fluctuations in average class sizes. Specifically, four out of the five courses experienced substantial increases in class size, with some nearly doubling in size (e.g., ENT_101 and CT_102), while one course (CT_101) showed a reduction in its average class size.

In this study, it must be noted that while some students enrolled in up to three different courses within the same academic year, none repeated the same exam in both years. Specifically, students who failed to pass their courses in 2020 were required to complete them in summer sessions and were consequently not included in this study’s dataset. To ensure clarity and precision in our analysis, the research focused exclusively on student test scores to evaluate and compare the academic effectiveness of online and traditional in-person learning methods. This approach was chosen to provide a clear, direct comparison of the educational impacts associated with each teaching format.

Descriptive analysis of the final exam scores for the two years (2020 and 2021) were conducted. Additionally, comparison of student outcomes in in-person classes in 2020 to their online platform peers in 2021 were conducted using an independent-samples t -test. Subsequently, in order to address potential disparities between the two groups arising from variables such as gender, class size, and admission scores (which serve as an indicator of students’ academic aptitude and pre-enrollment knowledge), multiple regression analyses were conducted. In these multivariate analyses, outcomes of both in-person and online cohorts were assessed within their respective tracks. By carefully considering essential aforementioned variables linked to student performance, the study aimed to ensure a comprehensive and equitable evaluation.

Study instrument

The study obtained students’ final exam scores for the years 2020 (in-person) and 2021 (online) from the school’s records office through their examination management system. In the preparatory year at King Saud University, final exams for all courses are developed by committees composed of faculty members from each department. To ensure valid comparisons, the final exam questions, crafted by departmental committees of professors, remained consistent and uniform for the two years under examination.

Table 3 provides a comprehensive assessment of the reliability of all five tests included in our analysis. These tests exhibit a strong degree of internal consistency, with Cronbach’s alpha coefficients spanning a range from 0.77 to 0.86. This robust and consistent internal consistency measurement underscores the dependable nature of these tests, affirming their reliability and suitability for the study’s objectives.

In terms of assessing test validity, content validity was ensured through a thorough review by university subject matter experts, resulting in test items that align well with the content domain and learning objectives. Additionally, criterion-related validity was established by correlating students’ admissions test scores with their final required freshman test scores in the five subject areas, showing a moderate and acceptable relationship (0.37 to 0.56) between the test scores and the external admissions test. Finally, construct validity was confirmed through reviews by experienced subject instructors, leading to improvements in test content. With guidance from university subject experts, construct validity was established, affirming the effectiveness of the final tests in assessing students’ subject knowledge at the end of their coursework.

Collectively, these validity and reliability measures affirm the soundness and integrity of the final subject tests, establishing their suitability as effective assessment tools for evaluating students’ knowledge in their five mandatory freshman courses at King Saud University.

After obtaining research approval from the Research Committee at King Saud University, the coordinators of the five courses (CI_101, ENT_101, CT_101, CT_102, and FAJB_101) supplied the researchers with the final exam scores of all first-year preparatory year students at King Saud University for the initial semester of the academic years 2020 and 2021. The sample encompassed all students who had completed these five courses during both years, resulting in a total of 16,722 students forming the final group of participants.

Limitations

Several limitations warrant acknowledgment in this study. First, the research was conducted within a well-resourced major public university. As such, the experiences with online classes at other types of institutions (e.g., community colleges, private institutions) may vary significantly. Additionally, the limited data pertaining to in-class teaching practices and the diversity of learning activities across different courses represents a gap that could have provided valuable insights for a more thorough interpretation and explanation of the study’s findings.

To compare student achievement in the final tests in the five courses by year, independent-samples t -tests were conducted. Table 4 shows a statistically-significant drop in test scores from 2020 (in person) to 2021 (online) for all courses except CT_101. The biggest decline was with CT_102 with 3.58 points, and the smallest decline was with CI_101 with 0.18 points.

However, such simple comparison of means between the two years (via t -tests) by subjects does not account for the differences in gender composition, class size, and admission scores between the two academic years, all of which have been associated with student outcomes (e.g., Ho and Kelman, 2014 ; De Paola et al., 2013 ). To account for such potential confounding variables, multiple regressions were conducted to compare the 2 years’ results while controlling for these three factors associated with student achievement.

Table 5 presents the regression results, illustrating the variation in final exam scores between 2020 and 2021, while controlling for gender, class size, and admission scores. Importantly, these results diverge significantly from the outcomes obtained through independent-sample t -test analyses.

Taking into consideration the variables mentioned earlier, students in the 2021 online cohort demonstrated superior performance compared to their 2020 in-person counterparts in CI_101, FAJB_101, and CT_101, with score advantages of 0.89, 0.56, and 5.28 points, respectively. Conversely, in the case of ENT_101, online students in 2021 scored 0.69 points lower than their 2020 in-person counterparts. With CT_102, there were no statistically significant differences in final exam scores between the two cohorts of students.

The study sought to assess the effectiveness of distance learning compared to in-person learning in the higher education setting in Saudi Arabia. We analyzed the final exam scores of 16,722 first-year college students in King Saud University in five required subjects (i.e., CI_101, ENT_101, CT_101, CT_102, and FAJB_101). The study initially performed a simple comparison of mean scores by tracks by year (via t -tests) and then a number of multiple regression analyses which controlled for class size, gender composition, and admission scores.

Overall, the study’s more in-depth findings using multiple regression painted a wholly different picture than the results obtained using t -tests. After controlling for class size, gender composition, and admissions scores, online students in 2021 performed better than their in-person instruction peers in 2020 in University Skills (CI_101), Fitness and Health (FAJB_101), and Computer Skills (CT_101), whereas in-person students outperformed their online peers in Entrepreneurship (ENT_101). There was no meaningful difference in outcomes for students in the Computer Skills (CT_102) course for the two years.

In light of these findings, it raises the question: why do we observe minimal differences (less than a one-point gain or loss) in student outcomes in courses like University Skills, Fitness and Health, Entrepreneurship, and Advanced Computer Skills based on the mode of instruction? Is it possible that when subjects are primarily at a basic or introductory level, as is the case with these courses, the mode of instruction may have a limited impact as long as the concepts are effectively communicated in a manner familiar and accessible to students?

In today’s digital age, one could argue that students in more developed countries, such as Saudi Arabia, generally possess the skills and capabilities to effectively engage with materials presented in both in-person and online formats. However, there is a notable exception in the Basic Computer Skills course, where the online cohort outperformed their in-person counterparts by more than 5 points. Insights from interviews with the instructors of this course suggest that this result may be attributed to the course’s basic and conceptual nature, coupled with the availability of instructional videos that students could revisit at their own pace.

Given that students enter this course with varying levels of computer skills, self-paced learning may have allowed them to cover course materials at their preferred speed, concentrating on less familiar topics while swiftly progressing through concepts they already understood. The advantages of such self-paced learning have been documented by scholars like Tullis and Benjamin ( 2011 ), who found that self-paced learners often outperform those who spend the same amount of time studying identical materials. This approach allows learners to allocate their time more effectively according to their individual learning pace, providing greater ownership and control over their learning experience. As such, in courses like introductory computer skills, it can be argued that becoming familiar with fundamental and conceptual topics may not require extensive in-class collaboration. Instead, it may be more about exposure to and digestion of materials in a format and at a pace tailored to students with diverse backgrounds, knowledge levels, and skill sets.

Further investigation is needed to more fully understand why some classes benefitted from online instruction while others did not, and vice versa. Perhaps, it could be posited that some content areas are more conducive to in-person (or online) format while others are not. Or it could be that the different results of the two modes of learning were driven by students of varying academic abilities and engagement, with low-achieving students being more vulnerable to the limitations of online learning (e.g., Kofoed et al., 2021 ). Whatever the reasons, the results of the current study can be enlightened by a more in-depth analysis of the various factors associated with such different forms of learning. Moreover, although not clear cut, what the current study does provide is additional evidence against any dire consequences to student learning (at least in the higher ed setting) as a result of sudden increase in online learning with possible benefits of its wider use being showcased.

Based on the findings of this study, we recommend that educational leaders adopt a measured approach to online learning—a stance that neither fully embraces nor outright denounces it. The impact on students’ experiences and engagement appears to vary depending on the subjects and methods of instruction, sometimes hindering, other times promoting effective learning, while some classes remain relatively unaffected.

Rather than taking a one-size-fits-all approach, educational leaders should be open to exploring the nuances behind these outcomes. This involves examining why certain courses thrived with online delivery, while others either experienced a decline in student achievement or remained largely unaffected. By exploring these differentiated outcomes associated with diverse instructional formats, leaders in higher education institutions and beyond can make informed decisions about resource allocation. For instance, resources could be channeled towards in-person learning for courses that benefit from it, while simultaneously expanding online access for courses that have demonstrated improved outcomes through its virtual format. This strategic approach not only optimizes resource allocation but could also open up additional revenue streams for the institution.

Considering the enduring presence of online learning, both before the pandemic and its accelerated adoption due to Covid-19, there is an increasing need for institutions of learning and scholars in higher education, as well as other fields, to prioritize the study of its effects and optimal utilization. This study, which compares student outcomes between two cohorts exposed to in-person and online instruction (before and during Covid-19) at the largest university in Saudi Arabia, represents a meaningful step in this direction.

Data availability

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Alarifi, B.N., Song, S. Online vs in-person learning in higher education: effects on student achievement and recommendations for leadership. Humanit Soc Sci Commun 11 , 86 (2024). https://doi.org/10.1057/s41599-023-02590-1

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A systematic review of research on online teaching and learning from 2009 to 2018

Associated data.

Systematic reviews were conducted in the nineties and early 2000's on online learning research. However, there is no review examining the broader aspect of research themes in online learning in the last decade. This systematic review addresses this gap by examining 619 research articles on online learning published in twelve journals in the last decade. These studies were examined for publication trends and patterns, research themes, research methods, and research settings and compared with the research themes from the previous decades. While there has been a slight decrease in the number of studies on online learning in 2015 and 2016, it has then continued to increase in 2017 and 2018. The majority of the studies were quantitative in nature and were examined in higher education. Online learning research was categorized into twelve themes and a framework across learner, course and instructor, and organizational levels was developed. Online learner characteristics and online engagement were examined in a high number of studies and were consistent with three of the prior systematic reviews. However, there is still a need for more research on organization level topics such as leadership, policy, and management and access, culture, equity, inclusion, and ethics and also on online instructor characteristics.

• • Twelve online learning research themes were identified in 2009–2018.
• • A framework with learner, course and instructor, and organizational levels was used.
• • Online learner characteristics and engagement were the mostly examined themes.
• • The majority of the studies used quantitative research methods and in higher education.
• • There is a need for more research on organization level topics.

1. Introduction

Online learning has been on the increase in the last two decades. In the United States, though higher education enrollment has declined, online learning enrollment in public institutions has continued to increase ( Allen & Seaman, 2017 ), and so has the research on online learning. There have been review studies conducted on specific areas on online learning such as innovations in online learning strategies ( Davis et al., 2018 ), empirical MOOC literature ( Liyanagunawardena et al., 2013 ; Veletsianos & Shepherdson, 2016 ; Zhu et al., 2018 ), quality in online education ( Esfijani, 2018 ), accessibility in online higher education ( Lee, 2017 ), synchronous online learning ( Martin et al., 2017 ), K-12 preparation for online teaching ( Moore-Adams et al., 2016 ), polychronicity in online learning ( Capdeferro et al., 2014 ), meaningful learning research in elearning and online learning environments ( Tsai, Shen, & Chiang, 2013 ), problem-based learning in elearning and online learning environments ( Tsai & Chiang, 2013 ), asynchronous online discussions ( Thomas, 2013 ), self-regulated learning in online learning environments ( Tsai, Shen, & Fan, 2013 ), game-based learning in online learning environments ( Tsai & Fan, 2013 ), and online course dropout ( Lee & Choi, 2011 ). While there have been review studies conducted on specific online learning topics, very few studies have been conducted on the broader aspect of online learning examining research themes.

2. Systematic Reviews of Distance Education and Online Learning Research

Distance education has evolved from offline to online settings with the access to internet and COVID-19 has made online learning the common delivery method across the world. Tallent-Runnels et al. (2006) reviewed research late 1990's to early 2000's, Berge and Mrozowski (2001) reviewed research 1990 to 1999, and Zawacki-Richter et al. (2009) reviewed research in 2000–2008 on distance education and online learning. Table 1 shows the research themes from previous systematic reviews on online learning research. There are some themes that re-occur in the various reviews, and there are also new themes that emerge. Though there have been reviews conducted in the nineties and early 2000's, there is no review examining the broader aspect of research themes in online learning in the last decade. Hence, the need for this systematic review which informs the research themes in online learning from 2009 to 2018. In the following sections, we review these systematic review studies in detail.

Comparison of online learning research themes from previous studies.

2.1. Distance education research themes, 1990 to 1999 ( Berge & Mrozowski, 2001 )

Berge and Mrozowski (2001) reviewed 890 research articles and dissertation abstracts on distance education from 1990 to 1999. The four distance education journals chosen by the authors to represent distance education included, American Journal of Distance Education, Distance Education, Open Learning, and the Journal of Distance Education. This review overlapped in the dates of the Tallent-Runnels et al. (2006) study. Berge and Mrozowski (2001) categorized the articles according to Sherry's (1996) ten themes of research issues in distance education: redefining roles of instructor and students, technologies used, issues of design, strategies to stimulate learning, learner characteristics and support, issues related to operating and policies and administration, access and equity, and costs and benefits.

In the Berge and Mrozowski (2001) study, more than 100 studies focused on each of the three themes: (1) design issues, (2) learner characteristics, and (3) strategies to increase interactivity and active learning. By design issues, the authors focused on instructional systems design and focused on topics such as content requirement, technical constraints, interactivity, and feedback. The next theme, strategies to increase interactivity and active learning, were closely related to design issues and focused on students’ modes of learning. Learner characteristics focused on accommodating various learning styles through customized instructional theory. Less than 50 studies focused on the three least examined themes: (1) cost-benefit tradeoffs, (2) equity and accessibility, and (3) learner support. Cost-benefit trade-offs focused on the implementation costs of distance education based on school characteristics. Equity and accessibility focused on the equity of access to distance education systems. Learner support included topics such as teacher to teacher support as well as teacher to student support.

2.2. Online learning research themes, 1993 to 2004 ( Tallent-Runnels et al., 2006 )

Tallent-Runnels et al. (2006) reviewed research on online instruction from 1993 to 2004. They reviewed 76 articles focused on online learning by searching five databases, ERIC, PsycINFO, ContentFirst, Education Abstracts, and WilsonSelect. Tallent-Runnels et al. (2006) categorized research into four themes, (1) course environment, (2) learners' outcomes, (3) learners’ characteristics, and (4) institutional and administrative factors. The first theme that the authors describe as course environment ( n  = 41, 53.9%) is an overarching theme that includes classroom culture, structural assistance, success factors, online interaction, and evaluation.

Tallent-Runnels et al. (2006) for their second theme found that studies focused on questions involving the process of teaching and learning and methods to explore cognitive and affective learner outcomes ( n  = 29, 38.2%). The authors stated that they found the research designs flawed and lacked rigor. However, the literature comparing traditional and online classrooms found both delivery systems to be adequate. Another research theme focused on learners’ characteristics ( n  = 12, 15.8%) and the synergy of learners, design of the online course, and system of delivery. Research findings revealed that online learners were mainly non-traditional, Caucasian, had different learning styles, and were highly motivated to learn. The final theme that they reported was institutional and administrative factors (n  = 13, 17.1%) on online learning. Their findings revealed that there was a lack of scholarly research in this area and most institutions did not have formal policies in place for course development as well as faculty and student support in training and evaluation. Their research confirmed that when universities offered online courses, it improved student enrollment numbers.

2.3. Distance education research themes 2000 to 2008 ( Zawacki-Richter et al., 2009 )

Zawacki-Richter et al. (2009) reviewed 695 articles on distance education from 2000 to 2008 using the Delphi method for consensus in identifying areas and classified the literature from five prominent journals. The five journals selected due to their wide scope in research in distance education included Open Learning, Distance Education, American Journal of Distance Education, the Journal of Distance Education, and the International Review of Research in Open and Distributed Learning. The reviewers examined the main focus of research and identified gaps in distance education research in this review.

Zawacki-Richter et al. (2009) classified the studies into macro, meso and micro levels focusing on 15 areas of research. The five areas of the macro-level addressed: (1) access, equity and ethics to deliver distance education for developing nations and the role of various technologies to narrow the digital divide, (2) teaching and learning drivers, markets, and professional development in the global context, (3) distance delivery systems and institutional partnerships and programs and impact of hybrid modes of delivery, (4) theoretical frameworks and models for instruction, knowledge building, and learner interactions in distance education practice, and (5) the types of preferred research methodologies. The meso-level focused on seven areas that involve: (1) management and organization for sustaining distance education programs, (2) examining financial aspects of developing and implementing online programs, (3) the challenges and benefits of new technologies for teaching and learning, (4) incentives to innovate, (5) professional development and support for faculty, (6) learner support services, and (7) issues involving quality standards and the impact on student enrollment and retention. The micro-level focused on three areas: (1) instructional design and pedagogical approaches, (2) culturally appropriate materials, interaction, communication, and collaboration among a community of learners, and (3) focus on characteristics of adult learners, socio-economic backgrounds, learning preferences, and dispositions.

The top three research themes in this review by Zawacki-Richter et al. (2009) were interaction and communities of learning ( n  = 122, 17.6%), instructional design ( n  = 121, 17.4%) and learner characteristics ( n  = 113, 16.3%). The lowest number of studies (less than 3%) were found in studies examining the following research themes, management and organization ( n  = 18), research methods in DE and knowledge transfer ( n  = 13), globalization of education and cross-cultural aspects ( n  = 13), innovation and change ( n  = 13), and costs and benefits ( n  = 12).

2.4. Online learning research themes

These three systematic reviews provide a broad understanding of distance education and online learning research themes from 1990 to 2008. However, there is an increase in the number of research studies on online learning in this decade and there is a need to identify recent research themes examined. Based on the previous systematic reviews ( Berge & Mrozowski, 2001 ; Hung, 2012 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ), online learning research in this study is grouped into twelve different research themes which include Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes. Table 2 below describes each of the research themes and using these themes, a framework is derived in Fig. 1 .

Research themes in online learning.

Online learning research themes framework.

The collection of research themes is presented as a framework in Fig. 1 . The themes are organized by domain or level to underscore the nested relationship that exists. As evidenced by the assortment of themes, research can focus on any domain of delivery or associated context. The “Learner” domain captures characteristics and outcomes related to learners and their interaction within the courses. The “Course and Instructor” domain captures elements about the broader design of the course and facilitation by the instructor, and the “Organizational” domain acknowledges the contextual influences on the course. It is important to note as well that due to the nesting, research themes can cross domains. For example, the broader cultural context may be studied as it pertains to course design and development, and institutional support can include both learner support and instructor support. Likewise, engagement research can involve instructors as well as learners.

In this introduction section, we have reviewed three systematic reviews on online learning research ( Berge & Mrozowski, 2001 ; Tallent-Runnels et al., 2006 ; Zawacki-Richter et al., 2009 ). Based on these reviews and other research, we have derived twelve themes to develop an online learning research framework which is nested in three levels: learner, course and instructor, and organization.

2.5. Purpose of this research

In two out of the three previous reviews, design, learner characteristics and interaction were examined in the highest number of studies. On the other hand, cost-benefit tradeoffs, equity and accessibility, institutional and administrative factors, and globalization and cross-cultural aspects were examined in the least number of studies. One explanation for this may be that it is a function of nesting, noting that studies falling in the Organizational and Course levels may encompass several courses or many more participants within courses. However, while some research themes re-occur, there are also variations in some themes across time, suggesting the importance of research themes rise and fall over time. Thus, a critical examination of the trends in themes is helpful for understanding where research is needed most. Also, since there is no recent study examining online learning research themes in the last decade, this study strives to address that gap by focusing on recent research themes found in the literature, and also reviewing research methods and settings. Notably, one goal is to also compare findings from this decade to the previous review studies. Overall, the purpose of this study is to examine publication trends in online learning research taking place during the last ten years and compare it with the previous themes identified in other review studies. Due to the continued growth of online learning research into new contexts and among new researchers, we also examine the research methods and settings found in the studies of this review.

The following research questions are addressed in this study.

• 1. What percentage of the population of articles published in the journals reviewed from 2009 to 2018 were related to online learning and empirical?
• 2. What is the frequency of online learning research themes in the empirical online learning articles of journals reviewed from 2009 to 2018?
• 3. What is the frequency of research methods and settings that researchers employed in the empirical online learning articles of the journals reviewed from 2009 to 2018?

This five-step systematic review process described in the U.S. Department of Education, Institute of Education Sciences, What Works Clearinghouse Procedures and Standards Handbook, Version 4.0 ( 2017 ) was used in this systematic review: (a) developing the review protocol, (b) identifying relevant literature, (c) screening studies, (d) reviewing articles, and (e) reporting findings.

3.1. Data sources and search strategies

The Education Research Complete database was searched using the keywords below for published articles between the years 2009 and 2018 using both the Title and Keyword function for the following search terms.

“online learning" OR "online teaching" OR "online program" OR "online course" OR “online education”

3.2. Inclusion/exclusion criteria

The initial search of online learning research among journals in the database resulted in more than 3000 possible articles. Therefore, we limited our search to select journals that focus on publishing peer-reviewed online learning and educational research. Our aim was to capture the journals that published the most articles in online learning. However, we also wanted to incorporate the concept of rigor, so we used expert perception to identify 12 peer-reviewed journals that publish high-quality online learning research. Dissertations and conference proceedings were excluded. To be included in this systematic review, each study had to meet the screening criteria as described in Table 3 . A research study was excluded if it did not meet all of the criteria to be included.

Inclusion/Exclusion criteria.

3.3. Process flow selection of articles

Fig. 2 shows the process flow involved in the selection of articles. The search in the database Education Research Complete yielded an initial sample of 3332 articles. Targeting the 12 journals removed 2579 articles. After reviewing the abstracts, we removed 134 articles based on the inclusion/exclusion criteria. The final sample, consisting of 619 articles, was entered into the computer software MAXQDA ( VERBI Software, 2019 ) for coding.

Flowchart of online learning research selection.

3.4. Developing review protocol

A review protocol was designed as a codebook in MAXQDA ( VERBI Software, 2019 ) by the three researchers. The codebook was developed based on findings from the previous review studies and from the initial screening of the articles in this review. The codebook included 12 research themes listed earlier in Table 2 (Learner characteristics, Instructor characteristics, Course or program design and development, Course Facilitation, Engagement, Course Assessment, Course Technologies, Access, Culture, Equity, Inclusion, and Ethics, Leadership, Policy and Management, Instructor and Learner Support, and Learner Outcomes), four research settings (higher education, continuing education, K-12, corporate/military), and three research designs (quantitative, qualitative and mixed methods). Fig. 3 below is a screenshot of MAXQDA used for the coding process.

Codebook from MAXQDA.

3.5. Data coding

Research articles were coded by two researchers in MAXQDA. Two researchers independently coded 10% of the articles and then discussed and updated the coding framework. The second author who was a doctoral student coded the remaining studies. The researchers met bi-weekly to address coding questions that emerged. After the first phase of coding, we found that more than 100 studies fell into each of the categories of Learner Characteristics or Engagement, so we decided to pursue a second phase of coding and reexamine the two themes. Learner Characteristics were classified into the subthemes of Academic, Affective, Motivational, Self-regulation, Cognitive, and Demographic Characteristics. Engagement was classified into the subthemes of Collaborating, Communication, Community, Involvement, Interaction, Participation, and Presence.

3.6. Data analysis

Frequency tables were generated for each of the variables so that outliers could be examined and narrative data could be collapsed into categories. Once cleaned and collapsed into a reasonable number of categories, descriptive statistics were used to describe each of the coded elements. We first present the frequencies of publications related to online learning in the 12 journals. The total number of articles for each journal (collectively, the population) was hand-counted from journal websites, excluding editorials and book reviews. The publication trend of online learning research was also depicted from 2009 to 2018. Then, the descriptive information of the 12 themes, including the subthemes of Learner Characteristics and Engagement were provided. Finally, research themes by research settings and methodology were elaborated.

4.1. Publication trends on online learning

Publication patterns of the 619 articles reviewed from the 12 journals are presented in Table 4 . International Review of Research in Open and Distributed Learning had the highest number of publications in this review. Overall, about 8% of the articles appearing in these twelve journals consisted of online learning publications; however, several journals had concentrations of online learning articles totaling more than 20%.

Empirical online learning research articles by journal, 2009–2018.

Note . Journal's Total Article count excludes reviews and editorials.

The publication trend of online learning research is depicted in Fig. 4 . When disaggregated by year, the total frequency of publications shows an increasing trend. Online learning articles increased throughout the decade and hit a relative maximum in 2014. The greatest number of online learning articles ( n  = 86) occurred most recently, in 2018.

Online learning publication trends by year.

4.2. Online learning research themes that appeared in the selected articles

The publications were categorized into the twelve research themes identified in Fig. 1 . The frequency counts and percentages of the research themes are provided in Table 5 below. A majority of the research is categorized into the Learner domain. The fewest number of articles appears in the Organization domain.

Research themes in the online learning publications from 2009 to 2018.

The specific themes of Engagement ( n  = 179, 28.92%) and Learner Characteristics ( n  = 134, 21.65%) were most often examined in publications. These two themes were further coded to identify sub-themes, which are described in the next two sections. Publications focusing on Instructor Characteristics ( n  = 21, 3.39%) were least common in the dataset.

4.2.1. Research on engagement

The largest number of studies was on engagement in online learning, which in the online learning literature is referred to and examined through different terms. Hence, we explore this category in more detail. In this review, we categorized the articles into seven different sub-themes as examined through different lenses including presence, interaction, community, participation, collaboration, involvement, and communication. We use the term “involvement” as one of the terms since researchers sometimes broadly used the term engagement to describe their work without further description. Table 6 below provides the description, frequency, and percentages of the various studies related to engagement.

Research sub-themes on engagement.

In the sections below, we provide several examples of the different engagement sub-themes that were studied within the larger engagement theme.

Presence. This sub-theme was the most researched in engagement. With the development of the community of inquiry framework most of the studies in this subtheme examined social presence ( Akcaoglu & Lee, 2016 ; Phirangee & Malec, 2017 ; Wei et al., 2012 ), teaching presence ( Orcutt & Dringus, 2017 ; Preisman, 2014 ; Wisneski et al., 2015 ) and cognitive presence ( Archibald, 2010 ; Olesova et al., 2016 ).

Interaction . This was the second most studied theme under engagement. Researchers examined increasing interpersonal interactions ( Cung et al., 2018 ), learner-learner interactions ( Phirangee, 2016 ; Shackelford & Maxwell, 2012 ; Tawfik et al., 2018 ), peer-peer interaction ( Comer et al., 2014 ), learner-instructor interaction ( Kuo et al., 2014 ), learner-content interaction ( Zimmerman, 2012 ), interaction through peer mentoring ( Ruane & Koku, 2014 ), interaction and community building ( Thormann & Fidalgo, 2014 ), and interaction in discussions ( Ruane & Lee, 2016 ; Tibi, 2018 ).

Community. Researchers examined building community in online courses ( Berry, 2017 ), supporting a sense of community ( Jiang, 2017 ), building an online learning community of practice ( Cho, 2016 ), building an academic community ( Glazer & Wanstreet, 2011 ; Nye, 2015 ; Overbaugh & Nickel, 2011 ), and examining connectedness and rapport in an online community ( Bolliger & Inan, 2012 ; Murphy & Rodríguez-Manzanares, 2012 ; Slagter van Tryon & Bishop, 2012 ).

Participation. Researchers examined engagement through participation in a number of studies. Some of the topics include, participation patterns in online discussion ( Marbouti & Wise, 2016 ; Wise et al., 2012 ), participation in MOOCs ( Ahn et al., 2013 ; Saadatmand & Kumpulainen, 2014 ), features that influence students’ online participation ( Rye & Støkken, 2012 ) and active participation.

Collaboration. Researchers examined engagement through collaborative learning. Specific studies focused on cross-cultural collaboration ( Kumi-Yeboah, 2018 ; Yang et al., 2014 ), how virtual teams collaborate ( Verstegen et al., 2018 ), types of collaboration teams ( Wicks et al., 2015 ), tools for collaboration ( Boling et al., 2014 ), and support for collaboration ( Kopp et al., 2012 ).

Involvement. Researchers examined engaging learners through involvement in various learning activities ( Cundell & Sheepy, 2018 ), student engagement through various measures ( Dixson, 2015 ), how instructors included engagement to involve students in learning ( O'Shea et al., 2015 ), different strategies to engage the learner ( Amador & Mederer, 2013 ), and designed emotionally engaging online environments ( Koseoglu & Doering, 2011 ).

Communication. Researchers examined communication in online learning in studies using social network analysis ( Ergün & Usluel, 2016 ), using informal communication tools such as Facebook for class discussion ( Kent, 2013 ), and using various modes of communication ( Cunningham et al., 2010 ; Rowe, 2016 ). Studies have also focused on both asynchronous and synchronous aspects of communication ( Swaggerty & Broemmel, 2017 ; Yamagata-Lynch, 2014 ).

4.2.2. Research on learner characteristics

The second largest theme was learner characteristics. In this review, we explore this further to identify several aspects of learner characteristics. In this review, we categorized the learner characteristics into self-regulation characteristics, motivational characteristics, academic characteristics, affective characteristics, cognitive characteristics, and demographic characteristics. Table 7 provides the number of studies and percentages examining the various learner characteristics.

Research sub-themes on learner characteristics.

Online learning has elements that are different from the traditional face-to-face classroom and so the characteristics of the online learners are also different. Yukselturk and Top (2013) categorized online learner profile into ten aspects: gender, age, work status, self-efficacy, online readiness, self-regulation, participation in discussion list, participation in chat sessions, satisfaction, and achievement. Their categorization shows that there are differences in online learner characteristics in these aspects when compared to learners in other settings. Some of the other aspects such as participation and achievement as discussed by Yukselturk and Top (2013) are discussed in different research themes in this study. The sections below provide examples of the learner characteristics sub-themes that were studied.

Self-regulation. Several researchers have examined self-regulation in online learning. They found that successful online learners are academically motivated ( Artino & Stephens, 2009 ), have academic self-efficacy ( Cho & Shen, 2013 ), have grit and intention to succeed ( Wang & Baker, 2018 ), have time management and elaboration strategies ( Broadbent, 2017 ), set goals and revisit course content ( Kizilcec et al., 2017 ), and persist ( Glazer & Murphy, 2015 ). Researchers found a positive relationship between learner's self-regulation and interaction ( Delen et al., 2014 ) and self-regulation and communication and collaboration ( Barnard et al., 2009 ).

Motivation. Researchers focused on motivation of online learners including different motivation levels of online learners ( Li & Tsai, 2017 ), what motivated online learners ( Chaiprasurt & Esichaikul, 2013 ), differences in motivation of online learners ( Hartnett et al., 2011 ), and motivation when compared to face to face learners ( Paechter & Maier, 2010 ). Harnett et al. (2011) found that online learner motivation was complex, multifaceted, and sensitive to situational conditions.

Academic. Several researchers have focused on academic aspects for online learner characteristics. Readiness for online learning has been examined as an academic factor by several researchers ( Buzdar et al., 2016 ; Dray et al., 2011 ; Wladis & Samuels, 2016 ; Yu, 2018 ) specifically focusing on creating and validating measures to examine online learner readiness including examining students emotional intelligence as a measure of student readiness for online learning. Researchers have also examined other academic factors such as academic standing ( Bradford & Wyatt, 2010 ), course level factors ( Wladis et al., 2014 ) and academic skills in online courses ( Shea & Bidjerano, 2014 ).

Affective. Anderson and Bourke (2013) describe affective characteristics through which learners express feelings or emotions. Several research studies focused on the affective characteristics of online learners. Learner satisfaction for online learning has been examined by several researchers ( Cole et al., 2014 ; Dziuban et al., 2015 ; Kuo et al., 2013 ; Lee, 2014a ) along with examining student emotions towards online assessment ( Kim et al., 2014 ).

Cognitive. Researchers have also examined cognitive aspects of learner characteristics including meta-cognitive skills, cognitive variables, higher-order thinking, cognitive density, and critical thinking ( Chen & Wu, 2012 ; Lee, 2014b ). Lee (2014b) examined the relationship between cognitive presence density and higher-order thinking skills. Chen and Wu (2012) examined the relationship between cognitive and motivational variables in an online system for secondary physical education.

Demographic. Researchers have examined various demographic factors in online learning. Several researchers have examined gender differences in online learning ( Bayeck et al., 2018 ; Lowes et al., 2016 ; Yukselturk & Bulut, 2009 ), ethnicity, age ( Ke & Kwak, 2013 ), and minority status ( Yeboah & Smith, 2016 ) of online learners.

4.2.3. Less frequently studied research themes

While engagement and learner characteristics were studied the most, other themes were less often studied in the literature and are presented here, according to size, with general descriptions of the types of research examined for each.

Evaluation and Quality Assurance. There were 38 studies (6.14%) published in the theme of evaluation and quality assurance. Some of the studies in this theme focused on course quality standards, using quality matters to evaluate quality, using the CIPP model for evaluation, online learning system evaluation, and course and program evaluations.

Course Technologies. There were 35 studies (5.65%) published in the course technologies theme. Some of the studies examined specific technologies such as Edmodo, YouTube, Web 2.0 tools, wikis, Twitter, WebCT, Screencasts, and Web conferencing systems in the online learning context.

Course Facilitation. There were 34 studies (5.49%) published in the course facilitation theme. Some of the studies in this theme examined facilitation strategies and methods, experiences of online facilitators, and online teaching methods.

Institutional Support. There were 33 studies (5.33%) published in the institutional support theme which included support for both the instructor and learner. Some of the studies on instructor support focused on training new online instructors, mentoring programs for faculty, professional development resources for faculty, online adjunct faculty training, and institutional support for online instructors. Studies on learner support focused on learning resources for online students, cognitive and social support for online learners, and help systems for online learner support.

Learner Outcome. There were 32 studies (5.17%) published in the learner outcome theme. Some of the studies that were examined in this theme focused on online learner enrollment, completion, learner dropout, retention, and learner success.

Course Assessment. There were 30 studies (4.85%) published in the course assessment theme. Some of the studies in the course assessment theme examined online exams, peer assessment and peer feedback, proctoring in online exams, and alternative assessments such as eportfolio.

Access, Culture, Equity, Inclusion, and Ethics. There were 29 studies (4.68%) published in the access, culture, equity, inclusion, and ethics theme. Some of the studies in this theme examined online learning across cultures, multi-cultural effectiveness, multi-access, and cultural diversity in online learning.

Leadership, Policy, and Management. There were 27 studies (4.36%) published in the leadership, policy, and management theme. Some of the studies on leadership, policy, and management focused on online learning leaders, stakeholders, strategies for online learning leadership, resource requirements, university policies for online course policies, governance, course ownership, and faculty incentives for online teaching.

Course Design and Development. There were 27 studies (4.36%) published in the course design and development theme. Some of the studies examined in this theme focused on design elements, design issues, design process, design competencies, design considerations, and instructional design in online courses.

Instructor Characteristics. There were 21 studies (3.39%) published in the instructor characteristics theme. Some of the studies in this theme were on motivation and experiences of online instructors, ability to perform online teaching duties, roles of online instructors, and adjunct versus full-time online instructors.

4.3. Research settings and methodology used in the studies

The research methods used in the studies were classified into quantitative, qualitative, and mixed methods ( Harwell, 2012 , pp. 147–163). The research setting was categorized into higher education, continuing education, K-12, and corporate/military. As shown in Table A in the appendix, the vast majority of the publications used higher education as the research setting ( n  = 509, 67.6%). Table B in the appendix shows that approximately half of the studies adopted the quantitative method ( n  = 324, 43.03%), followed by the qualitative method ( n  = 200, 26.56%). Mixed methods account for the smallest portion ( n  = 95, 12.62%).

Table A shows that the patterns of the four research settings were approximately consistent across the 12 themes except for the theme of Leaner Outcome and Institutional Support. Continuing education had a higher relative frequency in Learner Outcome (0.28) and K-12 had a higher relative frequency in Institutional Support (0.33) compared to the frequencies they had in the total themes (0.09 and 0.08 respectively). Table B in the appendix shows that the distribution of the three methods were not consistent across the 12 themes. While quantitative studies and qualitative studies were roughly evenly distributed in Engagement, they had a large discrepancy in Learner Characteristics. There were 100 quantitative studies; however, only 18 qualitative studies published in the theme of Learner Characteristics.

In summary, around 8% of the articles published in the 12 journals focus on online learning. Online learning publications showed a tendency of increase on the whole in the past decade, albeit fluctuated, with the greatest number occurring in 2018. Among the 12 research themes related to online learning, the themes of Engagement and Learner Characteristics were studied the most and the theme of Instructor Characteristics was studied the least. Most studies were conducted in the higher education setting and approximately half of the studies used the quantitative method. Looking at the 12 themes by setting and method, we found that the patterns of the themes by setting or by method were not consistent across the 12 themes.

The quality of our findings was ensured by scientific and thorough searches and coding consistency. The selection of the 12 journals provides evidence of the representativeness and quality of primary studies. In the coding process, any difficulties and questions were resolved by consultations with the research team at bi-weekly meetings, which ensures the intra-rater and interrater reliability of coding. All these approaches guarantee the transparency and replicability of the process and the quality of our results.

5. Discussion

This review enabled us to identify the online learning research themes examined from 2009 to 2018. In the section below, we review the most studied research themes, engagement and learner characteristics along with implications, limitations, and directions for future research.

5.1. Most studied research themes

Three out of the four systematic reviews informing the design of the present study found that online learner characteristics and online engagement were examined in a high number of studies. In this review, about half of the studies reviewed (50.57%) focused on online learner characteristics or online engagement. This shows the continued importance of these two themes. In the Tallent-Runnels et al.’s (2006) study, the learner characteristics theme was identified as least studied for which they state that researchers are beginning to investigate learner characteristics in the early days of online learning.

One of the differences found in this review is that course design and development was examined in the least number of studies in this review compared to two prior systematic reviews ( Berge & Mrozowski, 2001 ; Zawacki-Richter et al., 2009 ). Zawacki-Richter et al. did not use a keyword search but reviewed all the articles in five different distance education journals. Berge and Mrozowski (2001) included a research theme called design issues to include all aspects of instructional systems design in distance education journals. In our study, in addition to course design and development, we also had focused themes on learner outcomes, course facilitation, course assessment and course evaluation. These are all instructional design focused topics and since we had multiple themes focusing on instructional design topics, the course design and development category might have resulted in fewer studies. There is still a need for more studies to focus on online course design and development.

5.2. Least frequently studied research themes

Three out of the four systematic reviews discussed in the opening of this study found management and organization factors to be least studied. In this review, Leadership, Policy, and Management was studied among 4.36% of the studies and Access, Culture, Equity, Inclusion, and Ethics was studied among 4.68% of the studies in the organizational level. The theme on Equity and accessibility was also found to be the least studied theme in the Berge and Mrozowski (2001) study. In addition, instructor characteristics was the least examined research theme among the twelve themes studied in this review. Only 3.39% of the studies were on instructor characteristics. While there were some studies examining instructor motivation and experiences, instructor ability to teach online, online instructor roles, and adjunct versus full-time online instructors, there is still a need to examine topics focused on instructors and online teaching. This theme was not included in the prior reviews as the focus was more on the learner and the course but not on the instructor. While it is helpful to see research evolving on instructor focused topics, there is still a need for more research on the online instructor.

5.3. Comparing research themes from current study to previous studies

The research themes from this review were compared with research themes from previous systematic reviews, which targeted prior decades. Table 8 shows the comparison.

Comparison of most and least studied online learning research themes from current to previous reviews.

L = Learner, C=Course O=Organization.

5.4. Need for more studies on organizational level themes of online learning

In this review there is a greater concentration of studies focused on Learner domain topics, and reduced attention to broader more encompassing research themes that fall into the Course and Organization domains. There is a need for organizational level topics such as Access, Culture, Equity, Inclusion and Ethics, and Leadership, Policy and Management to be researched on within the context of online learning. Examination of access, culture, equity, inclusion and ethics is very important to support diverse online learners, particularly with the rapid expansion of online learning across all educational levels. This was also least studied based on Berge and Mrozowski (2001) systematic review.

The topics on leadership, policy and management were least studied both in this review and also in the Tallent-Runnels et al. (2006) and Zawacki-Richter et al. (2009) study. Tallent-Runnels categorized institutional and administrative aspects into institutional policies, institutional support, and enrollment effects. While we included support as a separate category, in this study leadership, policy and management were combined. There is still a need for research on leadership of those who manage online learning, policies for online education, and managing online programs. In the Zawacki-Richter et al. (2009) study, only a few studies examined management and organization focused topics. They also found management and organization to be strongly correlated with costs and benefits. In our study, costs and benefits were collectively included as an aspect of management and organization and not as a theme by itself. These studies will provide research-based evidence for online education administrators.

6. Limitations

As with any systematic review, there are limitations to the scope of the review. The search is limited to twelve journals in the field that typically include research on online learning. These manuscripts were identified by searching the Education Research Complete database which focuses on education students, professionals, and policymakers. Other discipline-specific journals as well as dissertations and proceedings were not included due to the volume of articles. Also, the search was performed using five search terms “online learning" OR "online teaching" OR "online program" OR "online course" OR “online education” in title and keyword. If authors did not include these terms, their respective work may have been excluded from this review even if it focused on online learning. While these terms are commonly used in North America, it may not be commonly used in other parts of the world. Additional studies may exist outside this scope.

The search strategy also affected how we presented results and introduced limitations regarding generalization. We identified that only 8% of the articles published in these journals were related to online learning; however, given the use of search terms to identify articles within select journals it was not feasible to identify the total number of research-based articles in the population. Furthermore, our review focused on the topics and general methods of research and did not systematically consider the quality of the published research. Lastly, some journals may have preferences for publishing studies on a particular topic or that use a particular method (e.g., quantitative methods), which introduces possible selection and publication biases which may skew the interpretation of results due to over/under representation. Future studies are recommended to include more journals to minimize the selection bias and obtain a more representative sample.

Certain limitations can be attributed to the coding process. Overall, the coding process for this review worked well for most articles, as each tended to have an individual or dominant focus as described in the abstracts, though several did mention other categories which likely were simultaneously considered to a lesser degree. However, in some cases, a dominant theme was not as apparent and an effort to create mutually exclusive groups for clearer interpretation the coders were occasionally forced to choose between two categories. To facilitate this coding, the full-texts were used to identify a study focus through a consensus seeking discussion among all authors. Likewise, some studies focused on topics that we have associated with a particular domain, but the design of the study may have promoted an aggregated examination or integrated factors from multiple domains (e.g., engagement). Due to our reliance on author descriptions, the impact of construct validity is likely a concern that requires additional exploration. Our final grouping of codes may not have aligned with the original author's description in the abstract. Additionally, coding of broader constructs which disproportionately occur in the Learner domain, such as learner outcomes, learner characteristics, and engagement, likely introduced bias towards these codes when considering studies that involved multiple domains. Additional refinement to explore the intersection of domains within studies is needed.

7. Implications and future research

One of the strengths of this review is the research categories we have identified. We hope these categories will support future researchers and identify areas and levels of need for future research. Overall, there is some agreement on research themes on online learning research among previous reviews and this one, at the same time there are some contradicting findings. We hope the most-researched themes and least-researched themes provide authors a direction on the importance of research and areas of need to focus on.

The leading themes found in this review is online engagement research. However, presentation of this research was inconsistent, and often lacked specificity. This is not unique to online environments, but the nuances of defining engagement in an online environment are unique and therefore need further investigation and clarification. This review points to seven distinct classifications of online engagement. Further research on engagement should indicate which type of engagement is sought. This level of specificity is necessary to establish instruments for measuring engagement and ultimately testing frameworks for classifying engagement and promoting it in online environments. Also, it might be of importance to examine the relationship between these seven sub-themes of engagement.

Additionally, this review highlights growing attention to learner characteristics, which constitutes a shift in focus away from instructional characteristics and course design. Although this is consistent with the focus on engagement, the role of the instructor, and course design with respect to these outcomes remains important. Results of the learner characteristics and engagement research paired with course design will have important ramifications for the use of teaching and learning professionals who support instruction. Additionally, the review also points to a concentration of research in the area of higher education. With an immediate and growing emphasis on online learning in K-12 and corporate settings, there is a critical need for further investigation in these settings.

Lastly, because the present review did not focus on the overall effect of interventions, opportunities exist for dedicated meta-analyses. Particular attention to research on engagement and learner characteristics as well as how these vary by study design and outcomes would be logical additions to the research literature.

8. Conclusion

This systematic review builds upon three previous reviews which tackled the topic of online learning between 1990 and 2010 by extending the timeframe to consider the most recent set of published research. Covering the most recent decade, our review of 619 articles from 12 leading online learning journal points to a more concentrated focus on the learner domain including engagement and learner characteristics, with more limited attention to topics pertaining to the classroom or organizational level. The review highlights an opportunity for the field to clarify terminology concerning online learning research, particularly in the areas of learner outcomes where there is a tendency to classify research more generally (e.g., engagement). Using this sample of published literature, we provide a possible taxonomy for categorizing this research using subcategories. The field could benefit from a broader conversation about how these categories can shape a comprehensive framework for online learning research. Such efforts will enable the field to effectively prioritize research aims over time and synthesize effects.

Credit author statement

Florence Martin: Conceptualization; Writing - original draft, Writing - review & editing Preparation, Supervision, Project administration. Ting Sun: Methodology, Formal analysis, Writing - original draft, Writing - review & editing. Carl Westine: Methodology, Formal analysis, Writing - original draft, Writing - review & editing, Supervision

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

1 Includes articles that are cited in this manuscript and also included in the systematic review. The entire list of 619 articles used in the systematic review can be obtained by emailing the authors.*

Appendix B Supplementary data to this article can be found online at https://doi.org/10.1016/j.compedu.2020.104009 .

Appendix A. 

Research Themes by the Settings in the Online Learning Publications

Research Themes by the Methodology in the Online Learning Publications

Appendix B. Supplementary data

The following are the Supplementary data to this article:

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The struggle for connection and other challenges in online distance learning

Blended learning has introduced a unique set of challenges for both teachers and students in public schools. The shift to online distance learning, initially adopted to continue education during the pandemic, has proven to be a complex adjustment for many. A significant issue encountered by teachers is the insufficient internet data available to students. Many learners start a lesson only to run out of data midway, causing them to drop out of class. This struggle for connection is further compounded by poor signal quality, which students often claim as the reason for not participating in recitations, or for the choppy communication during online classes. These technical difficulties create a barrier to effective learning and meaningful interaction.

Moreover, the flexibility of online learning can sometimes work against student engagement. Some learners are tempted to skip classes, citing poor internet connectivity as an excuse, while others are distracted by the comfort of their homes. The home environment, filled with potential distractions, competes with the focus required for schoolwork. This situation is exacerbated by the temptation to manage other personal matters during school hours, leading to further disengagement from the learning process.

In many schools across the country, the struggle for online connection is even more pronounced. Numerous public schools were asked to offer senior high school programs despite lacking the necessary facilities. Without adequate classrooms and resources, these schools resorted to blended learning as a solution. However, the lack of infrastructure makes it difficult to deliver quality education through this mode. Said one learner: “I feel like I’m not learning as much as those in schools that have full face-to-face classes. It’s frustrating because I want to do well, but it feels like I’m missing out on a lot.”

As a teacher, I can personally attest that this mode of learning is not effective and cannot be a substitute for face-to-face interaction. The in-person classroom experience is crucial for helping learners become engaged with and understand the material better. There is no replacement for the dynamic interactions and immediate feedback that occur in a physical classroom, where students are more focused and less prone to distractions. The investment in classrooms and physical infrastructure is essential to ensure that education is effective, and that students can learn in an environment conducive to their academic and personal development.

This struggle for connection highlights the difficulties teachers face in maintaining student participation and engagement in a blended learning setup. The lack of direct supervision and the myriad distractions at home make it difficult for students to remain committed to their studies, posing a significant hurdle to the success of blended learning in public schools.

Teachers are tasked not only with delivering educational content, but also with finding innovative ways to keep students engaged and accountable in a learning environment that is far less structured than the traditional classroom. Investing in classroom infrastructure and returning to face-to-face learning is a step toward ensuring that students receive the best possible education. While technology has its place, it should complement rather than replace the traditional classroom experience, which remains vital for student success.

Maria Teresa B. Macasinag, Rizal National High School, Baguio City

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