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

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Corresponding author.

Received 2020 Dec 6; Accepted 2021 Aug 30; Issue date 2022.

This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.

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.

Keywords: Online education, Student achievement, Academic success, Meta-analysis

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 ).

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.

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 .

The characteristics of the studies included in the meta-analysis

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.

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.

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).

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 .

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

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 ).

Reliability tests results representing the probability of publication bias

* Represents the required number of papers for Hedges g co-efficiency to reach a rate out of 0.01 range

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 .

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

The findings related to the effect size of online education on academic success

n: the Number of Studies included in Meta-Analysis; Hedges g: average effect size

p: significance level of the effect size; S error : standard error; EB low – EB up : lower and upper limits of the effect size

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 .

The dispersion of the studies according to the countries and the heterogeneity test results

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 .

The dispersion of the studies according to the class level and the heterogeneity test results

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 .

The dispersion of the studies according to the school subjects and the heterogeneity test results

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.

The dispersion of the studies according to the online education approaches and the heterogeneity test results

Conclusions and discussion

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

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

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

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

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

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

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

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

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

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

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

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*Studies included in meta-analysis

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Theories and Frameworks for Online Education: Seeking an Integrated Model

• Anthony G Picciano City University of New York, Hunter College

This article examines theoretical frameworks and models that focus on the pedagogical aspects of online education. After a review of learning theory as applied to online education, a proposal for an integrated Multimodal Model for Online Education is provided based on pedagogical purpose.  The model attempts to integrate the work of several other major theorists and model builders such as Anderson (2011).

Author Biography

Anthony g picciano, city university of new york, hunter college.

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Chapter 5 Theories and Frameworks for Online Education

Seeking an integrated model.

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In this chapter you will examine theoretical frameworks and models that focus on the pedagogical aspects of online education. After a review of learning theory as applied to online education, a proposal for an integrated Multimodal Model for Online Education is provided based on pedagogical purpose. The model attempts to integrate the work of several other major theorists and model builders such as Anderson (2011).

1 Introduction

In a provocative chapter of The Theory and Practice of Online Learning , Terry Anderson (2011) examines whether a common theory for online education can be developed. While recognizing that as a difficult, and perhaps fruitless, task, he nonetheless examines possibilities and proposes his own theory which he admits is not complete. The purpose of this article is to examine theoretical frameworks relevant to the pedagogical aspects of online education. It starts with a consideration of learning theories and funnels down to their specific application to online education. The article concludes with a proposal for an integrated model for online education based on pedagogical purpose.

2 Learning Theory

Learning theory is meant to explain and help us understand how people learn; however, the literature is complex and extensive enough to fill entire sections of a library. It involves multiple disciplines, including psychology, sociology, neuroscience, and of course, education. Three of the more popular learning theories – behaviorism, cognitivism, and social constructivism – will be highlighted to form the foundation for further discussion. Mention will also be made of several other learning theories that are relevant to online education. Before reviewing these theories, it will be worthwhile to have a brief discussion of the term theory itself.

Theory is defined as a set of statements, principles, or ideas that relate to a particular subject. A theory usually describes, explains, and/or predicts phenomena. The definition of theory also varies depending upon disciplines, especially when related to the term model. As noted by Graham, Henrie, and Gibbons (2013), the two terms are used interchangeably and generally refer to the same concept. However, a model is more frequently a visual representation of reality or a concept. In this discussion, the terms theory and model will be used interchangeably. The purpose of a theory or model is to propose the answers to basic questions associated with a phenomenon. Graham, Henrie and Gibbons (2013) reviewed this issue as related to instructional technology and recommended a three-part taxonomy first proposed by Gibbons and Bunderson (2005) that includes theories that:

• – Explore: “What exists?” and attempts to define [describe] and categorize;
• – Explain: “Why does this happen?” and looks for causality and correlation, and work with variables and relationships.
• – Design : “How do I achieve this outcome?” and describes interventions for reaching targeted outcomes and operational principles (Graham, Henrie, & Gibbons, 2013, p. 13).

This taxonomy will serve as an overall guiding principle for the discussion of learning theories and models in this article.

3 Behaviorism

As its name implies, behaviorism focuses on how people behave. It evolved from a positivist worldview related to cause and effect. In simple terms, action produces reaction. In education, behaviorism examines how students behave while learning. More specifically, behaviorism focuses on observing how students respond to certain stimuli that, when repeated, can be evaluated, quantified, and eventually controlled for each individual. The emphasis in behaviorism is on that which is observable and not on the mind or cognitive processes. In sum, if you cannot observe it, it cannot be studied.

The development of behaviorism is frequently associated with Ivan Pavlov, famous for his experiments with dogs, food, and audible stimuli, such as a bell. In his experiments, dogs learned to associate food or feeding time with the sound of the bell and began to salivate. Pavlov conducted his experiments in the early 1900s and they were replicated by many other researchers throughout the 20th century. John B. Watson, among the first Americans to follow Pavlov’s work, saw it as a branch of natural science. Watson became a major proponent of Pavlov and is generally credited with coining the term behaviorism. He argued that mind and consciousness are unimportant in the learning process and that everything can be studied in terms of stimulus and response.

Other major figures associated with behaviorism are B.F. Skinner and Edward Thorndike. Skinner is particularly well known, primarily because he introduced what he referred to as operant conditioning which emphasized the use of both positive and negative reinforcement to help individuals learn new behaviors. This was quite different from Pavlov, who relied on simple reflexive responses to specific stimuli although both Pavlov and Skinner promoted repetitive behavior that leads to habit formation. Skinner had a significant influence on early computer-assisted instructional ( CAI ) models as developed by Pat Suppes and others. A common aspect of early CAI programs was the reliance on encouragement and repetition to promote positive learning activities.

4 Cognitivism

Cognitivism has been considered a reaction to the “rigid” emphasis by behaviorists on predictive stimulus and response (Harasim, 2012, p. 58). Cognitive theorists promoted the concept that the mind has an important role in learning and sought to focus on what happens in between the occurrence of environmental stimulus and student response. They saw the cognitive processes of the mind, such as motivation and imagination, as critical elements of learning that bridge environmental stimuli and student responses. For example, Noam Chomsky (1959) wrote a critical review of Skinner’s behaviorist work in which he raised the importance of creative mental processes that are not observable in the physical world. Although written mainly from the perspective of a linguist, Chomsky’s view gained popularity in other fields, including psychology. Interdisciplinary in nature, cognitive science draws from psychology, biology, neuroscience, computer science, and philosophy to explain the workings of the brain as well as levels of cognitive development that form the foundation of learning and knowledge acquisition. As a result, cognitivism has evolved into one of the dominant learning theories. The future of cognitivism is particularly interesting as more advanced online software evolves into adaptive and personalized learning applications that seek to integrate artificial intelligence and learning analytics into instruction.

Behaviorism led to the development of taxonomies of learning because it emphasized the study and evaluation of multiple steps in the learning process. Behaviorists repeatedly studied learning activities to deconstruct and define the elements of learning. Benjamin Bloom (1956) was among the early psychologists to establish a taxonomy of learning that related to the development of intellectual skills and to stress the importance of problem solving as a higher order skill. Bloom’s (1956) Taxonomy of educational objectives handbook: Cognitive domains remains a foundational text and essential reading within the educational community. Bloom’s taxonomy is based on six key elements (see Figure 5.1 ) as follows:

• – Creating: Putting elements together to form a coherent or functional whole, and reorganizing elements into a new pattern or structure through generating, planning, or producing.
• – Evaluating: Making judgments based on criteria and standards through checking and critiquing.
• – Analyzing: Breaking material into constituent parts, and determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing.
• – Applying: Carrying out or using a procedure through executing or implementing.
• – Understanding: Constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining.
• – Remembering: Retrieving, recognizing, and recalling relevant knowledge from long-term memory.

Bloom’s taxonomy

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Bloom, in developing his taxonomy, essentially helped to move learning theory toward issues of cognition and developmental psychology. Twenty years later, Robert Gagne, an educational psychologist, developed another taxonomy (events of instruction) that built on Bloom’s and became the basis for cognitivist instructional design (Harasim, 2012). Gagne emphasized nine events in instruction that drive the definitions of objectives and strategies for the design of instructional material (see Figure 5.2 ).

Gagné’s nine events of instruction

5 social constructivism.

Parallel to behaviorism and cognitivism was the work of several education theorists, including Lev Vygotsky, John Dewey, and Jean Piaget. Their focus on social constructionism was to describe and explain teaching and learning as complex interactive social phenomena between teachers and students. Vygotsky posited that learning is problem solving and that the social construction of solutions to problems is the basis of the learning process. Vygotsky described the learning process as the establishment of a “zone of proximal development” in which the teacher, the learner, and a problem to be solved exist. The teacher provides a social environment in which the learner can assemble or construct with others the knowledge necessary to solve the problem. Likewise, John Dewey saw learning as a series of practical social experiences in which learners learn by doing, collaborating, and reflecting with others. While developed in the early part of the 20th century, Dewey’s work is very much in evidence in a good deal of present-day social constructivist instructional design. The use of reflective practice by both learner and teacher is a pedagogical cornerstone for interactive discussions that replaces straight lecturing, whether in a face-to-face or online class. Jean Piaget, whose background was in psychology and biology, based his learning theory on four stages of cognitive development that begin at birth and continue through one’s teen years and beyond. Seymour Papert, in designing the Logo programming language, drew from Jean Piaget the concept of creating social, interactive microworlds or communities where children, under the guidance of a teacher, solve problems while examining social issues, mathematical and science equations, or case studies. Papert’s approach of integrating computer technology into problem solving is easily applied to many facets of instructional design.

6 Derivatives of the Major Learning Theories

A number of theories and models have roots in one or more of the above frameworks. In the latter part of the 20th century, the major learning theories, especially cognitive theory and social constructivism, began to overlap. For example, Wenger and Lave (1991) and Wenger (1998) promoted concepts such as “communities of practice” and situated learning. Their position was that learning involves a deepening process situated in, and derived from, participation in a learning community of practice. Their work is very evident in many studies, including those related to online education.

Information processing learning theory is a variation of cognitivism that views the human mind as a system that processes information according to a set of logical rules. In it, the mind is frequently compared to a computer that follows a set of rules or program. Research using this perspective attempts to describe and explain changes in the mental processes and strategies that lead to greater cognitive competence as children develop. Richard Atkinson and Richard Shiffrin (1968) are generally credited with proposing the first information processing model that deals with how students acquire, encode, store (in short-term or long-term memory), and retrieve information.

One of the more popular and controversial theories relates to learning styles and posits that individuals learn differently depending upon their propensities and personalities. Carl Jung argued that individual personality types influence various elements of human behavior, including learning. Jung’s theory focuses on four basic psychological dimensions:

• – Extroversion vs. Introversion
• – Sensation vs. Intuition
• – Thinking vs. Feeling
• – Judging vs. Perceiving

While each unique dimension can influence an individual learning style, it is likely that learning styles are based on a combination of these dimensions. For example, a learning style might include elements of extroversion, sensation, feeling, and perception as personality dimensions. Readers may be familiar with the Myers-Briggs Type Inventory ( MBTI ) which has been used for decades to assist in determining personality types, including how personality relates to student learning. The MBTI is based extensively on Jung’s theories and has been used to predict and develop different teaching methods and environments and to predict individual patterns of mental functioning, such as information processing, idea development, and judgment formation. It can also be used to foretell patterns of attitudes and interests that influence an individual’s preferred learning environment and to predict a person’s disposition to pursue certain learning circumstances and avoid others. Lin, Cranton, and Bridglall (2005) remind us that much of the work of Carl Jung and the MBTI is applicable to learning environments, whether face-to-face or online. For example, the extrovert may prefer active, highly collaborative environments while the introvert would prefer less interaction and less collaboration. This suggests that instruction should be designed to allow both types of individuals – the outgoing social organizer as well as the introspective reflective observer – to thrive.

Howard Gardner has developed a theory of “multiple intelligences” that proposes that intelligence is not merely a singular entity but consists of multiple entities used by individuals in different proportions to understand and to learn about the world. Gardner has identified nine basic intelligences: linguistic, logical/mathematical, spatial, musical, bodily kinesthetic, interpersonal, intrapersonal, naturalistic, and existential (see Figure 5.3 ).

Gardner’s multiples intelligences (Source: Gardner, 1983)

Gardner’s theory has received criticism from both psychologists and educators who view these “intelligences” as talents, personality traits, and abilities. His work has also been questioned by those who propose that there is, in fact, a root or base intelligence that drives the other “intelligences.” Gardner does not necessarily disagree with this latter position but maintains that other intelligences can be viewed as main branches off the base root intelligence. This theory has important pedagogical implications and suggests the design of multiple learning modalities that allow learners to engage in ways they prefer, according to their interest or ability, and to challenge them to learn in other ways that are less related to their preferences, interests, or abilities. Gardner’s work also addresses the common concern that too much teaching and learning is linguistically based (reading, writing, and speaking) and that the other intelligences are underutilized.

Modern neuroscience research also suggests that students learn in different ways depending upon a number of factors including age, learning stimuli, and the pace of instruction. Willingham (2008) suggests that learning is a dynamic process that may evolve and change from one classroom to another, from one subject to another, and from one day to another. This research also supports the concept that multiple intelligences and mental abilities do not exist as mere “yes/no” entities but within continua which the mind blends in a manner consistent with the way it responds and learns from the external environment and instructional stimuli. Conceptually, this suggests a framework for a multimodal instructional design that relies on a variety of pedagogical techniques, delivery approaches, and media.

Lastly, Malcom Knowles (1998) deserves mention as the individual who distinguished between andragogy (adult learning) and pedagogy (child learning). Adults, whether seeking to enhance their professional skills or to satisfy curiosity about a subject, learn differently than children. Courses designed for adults should tap into their social contexts and experiences. Knowles’ insights are especially important for higher education, where online technology is used extensively for adult students in traditional and continuing education programs, competency-based learning, and career/professional development.

In sum, a number of theories have been, and will continue to be, applied to instruction, including online and blended learning. Several theories specifically related to online education will now be examined.

7 Learning Theories for Online Education

Just as no single learning theory has emerged for instruction in general, the same is true for online education. A number of theories have evolved, most of which derive from the major learning theories discussed previously. In this section, several theories will be examined in terms of their appropriateness for the online environment.

7.1 Community of Inquiry (CoI)

The “community of inquiry” model for online learning environments developed by Garrison, Anderson, and Archer (2000) is based on the concept of three distinct “presences”: cognitive, social, and teaching (see Figure 5.4 ). While recognizing the overlap and relationship among the three components, Anderson, Rourke, Garrison, and Archer (2001) advise further research on each component. Their model supports the design of online and blended courses as active learning environments or communities dependent on instructors and students sharing ideas, information, and opinions. Of particular note is that “presence” is a social phenomenon and manifests itself through interactions among students and instructors. The community of inquiry has become one of the more popular models for online and blended courses that are designed to be highly interactive among students and faculty using discussion boards, blogs, wikis, and videoconferencing.

Community of inquiry (from Garrison, Anderson, Garrison, & Archer, 2000).

7.2 connectivism.

George Siemens (2004), one of the early MOOC pioneers, has been the main proponent of connectivism, a learning model that acknowledges major shifts in the way knowledge and information flows, grows, and changes because of vast data communications networks. Internet technology has moved learning from internal, individualistic activities to group, community, and even crowd activities. In developing the theory, Siemens acknowledged the work of Alberto Barabasi and the power of networks. He also referenced an article written by Karen Stephensen (1998) entitled “What Knowledge Tears Apart, Networks Make Whole,” which accurately identified how large-scale networks become indispensable in helping people and organizations manage data and information.

Siemens describes connectivism as:

the integration of principles explored by chaos, network, and complexity and self-organization theories [where] learning is a process that occurs within nebulous environments of shifting core elements – not entirely under the control of the individual. Learning (defined as actionable knowledge) can reside outside of ourselves (within an organization or a database), is focused on connecting specialized information sets, and the connections that enable us to learn more and are more important than our current state of knowing. (Siemens, 2004)

Siemens noted that connectivism as a theory is driven by the dynamic of information flow. Students need to understand, and be provided with, experiences in navigating and recognizing oceans of constantly shifting and evolving information. Siemens proposed eight principles of connectivism (see Figure 5.5 ). Connectivism is particularly appropriate for courses with very high enrollments and where the learning goal or objective is to develop and create knowledge rather than to disseminate it.

Siemens’ eight principles of connectivism

7.3 online collaborative learning ( ocl ).

Online collaborative learning ( OCL ) is a theory proposed by Linda Harasim that focuses on the facilities of the Internet to provide learning environments that foster collaboration and knowledge building. Harasim (2012) describes OCL as:

a new theory of learning that focuses on collaborative learning, knowledge building, and Internet use as a means to reshape formal, non-formal, and informal education for the Knowledge Age. (p. 81)

Like Siemens, Harasim sees the benefits of moving teaching and learning to the Internet and large-scale networked education. In some respects, Harasim utilizes Alberto Barabasi’s position on the power of networks. In OCL , there exist three phases of knowledge construction through discourse in a group:

Idea generating: the brainstorming phase, where divergent thoughts are gathered

Idea organizing: the phase where ideas are compared, analyzed, and categorized through discussion and argument

Intellectual convergence: the phase where intellectual synthesis and consensus occurs, including agreeing to disagree, usually through an assignment, essay, or other joint piece of work (Harasim, 2012, p. 82).

OCL also derives from social constructivism, since students are encouraged to collaboratively solve problems through discourse and where the teacher plays the role of facilitator as well as learning community member. This is a major aspect of OCL but also of other constructivist theories where the teacher is not necessarily separate and apart but rather, an active facilitator of, knowledge building. Because of the importance of the role of the teacher, OCL is not easy to scale up. Unlike connectivism, which is suited for large-scale instruction, OCL is best situated in smaller instructional environments. This last issue becomes increasingly important when seeking commonality among online education theories.

Many other theories can be associated with online education but, rather than present more theories and in keeping with one of the major purposes of this article, it is appropriate to ask whether an integrated or unified theory of online education is possible.

8 Can We Build a Common Integrated Theory of Online Education?

As noted, Terry Anderson (2011) examined the possibility of building a theory of online education, starting with the assumption that it would be a difficult, and perhaps impossible, task. He approached this undertaking from a distance education perspective, having spent much of his career at Athabasca University, the major higher education distance education provider in Canada. While he acknowledged that many theorists and practitioners consider online learning as “a subset of learning in general” (Anderson, 2011, pp. 46–47), he also stated:

online learning as a subset of distance education has always been concerned with provision of access to educational experience that is, at least more flexible in time and in space as campus-based education. (Anderson, 2011, p. 53)

These two perspectives (subset of learning in general and subset of distance education) complicate any attempt to build a common theory of online education. Blended learning models, for instance, do not easily fit into the distance education schema, even though they are evolving as a prevalent component of traditional face-to-face and online education environments.

Anderson considered a number of theories and models but focused on the well-respected work of Bransford, Brown, and Cocking (1999) who posited that effective learning environments are framed within the convergence of four overlapping lenses: community-centeredness, knowledge-centeredness, learner-centeredness, and assessment centeredness. These lenses provided the foundational framework for Anderson’s approach to building an online education theory, as he examined in detail the characteristics and facilities that the Internet provides with regards to each of the four lenses. Second, he noted that the Internet had evolved from a text-based environment to one in which all forms of media are supported and readily available. He also accurately commented that the Internet’s hyperlink capacity is most compatible with the way human knowledge is stored and accessed. In this regard, he referred to the work of Jonassen (1992) and Shank (1993) who associated hyperlinking with constructivism. Finally, Anderson extensively examined the importance of interaction in all forms of learning and referred to a number of mostly distance education theorists such as Holmberg (1989), Moore (1989), Moore and Kearsley (1996), and Garrison and Shale (1990). The essence of interaction among students, teachers, and content is well understood and is referenced in many theories of education, especially constructivism. Anderson’s evaluation of interaction concludes that interactions are critical components of a theory.

With these three elements in mind (the Bransford, Brown, and Cocking lenses, the affordances and facilities of the Internet, and interaction), Anderson then proceeded to construct a model (see Figure 5.6 ). He did add one important element by distinguishing community/collaborative models from self-paced instructional models, commenting that community/collaborative models and self-paced instructional models are inherently incompatible. The community/collaborative models do not scale up easily because of the extensive interactions among teachers and students. On the other hand, the self-paced instructional models are designed for independent learning with much less interaction among students and teachers.

Figure 5.6 illustrates

Anderson’s online learning model (reprinted with permission from Anderson, 2011)

the two major human actors, learners and teachers, and their interactions with each other and with content. Learners can of course interact directly with content that they find in multiple formats, and especially on the Web; however, many choose to have their learning sequenced, directed, and evaluated with the assistance of a teacher. This interaction can take place within a community of inquiry, using a variety of Net-based synchronous and asynchronous activities … These environments are particularly rich, and allow for the learning of social skills, the collaborative learning of content, and the development of personal relationships among participants. However, the community binds learners in time, forcing regular sessions or at least group-paced learning. The second model of learning (on the right) illustrates the structured learning tools associated with independent learning. Common tools used in this mode include computer-assisted tutorials, drills, and simulations. (Anderson, 2011, pp. 61–62)

Figure 5.6 demonstrates the instructional flow within the two sides and represents the beginnings of a theory or model from the distance education perspective. Anderson concluded that his model “will help us to deepen our understanding of this complex educational context” (Anderson, 2011, p. 68), which he noted needs to measure more fully the direction and magnitude of each input variable on relevant outcome variables.

Anderson also commented about the potential of the Internet for education delivery, and that an online learning-based theory or model could subsume all other modes with the exception of the “rich face-to-face interaction in formal classrooms” (Anderson, 2011, p. 67). This becomes a quandary for Anderson in trying to develop a common theory of online education in that it does not provide for in-person, face-to-face activity and is problematic for those who see online education as a subset of education in general.

9 An Integrated Model

Anderson’s model assumed that none of the instruction is delivered in traditional, face-to-face mode, and so excluded blended learning models that have some face-to-face component. Is it possible, therefore, to approach the search for an integrated model for online education from the face-to-face education in general or even the blended learning perspective?

Bosch (2016), in a review of instructional technology, identified and compared four blended learning models using twenty-one different design components. These models emphasized, to one degree or another, the integration of pedagogy and technology in course design. Among the models was a Blending with Pedagogical Purpose Model (see Figure 5.7 ), developed by this author, in which pedagogical objectives and activities drive the approaches, including the online technology that faculty members use in instruction. The model also suggests that blending the objectives, activities, and approaches within multiple modalities might be most effective for, and appeal to, a wide range of students. The model contains six basic pedagogical goals, and approaches for achieving them, to form learning modules. The model is flexible and assumes that other modules can be added as needed and where appropriate. The most important feature of this model is that pedagogy drives the approaches that will work best to support student learning. The modules are also shown as intersecting but this is optional; they may or may not intersect or overlap depending upon the approaches used. For instance, some reflection can be incorporated into collaboration or not, depending upon how the collaborative activity is designed. It might be beneficial to have the collaborative groups reflect specifically on their activities. Similar scenarios are possible for the other modules. Ultimately important is that all the modules used blend together into a coherent whole. The following paragraphs briefly review each of these modules.

Blending with pedagogical purpose model

Content is one of the primary drivers of instruction and there are many ways in which content can be delivered and presented. While much of what is taught is delivered linguistically (teacher speaks/students listen or teacher writes/students write), this does not have to be the case, either in face-to-face or online environments. Mayer (2009) has done extensive reviews of the research and has concluded that learning is greatly enhanced by visualization. Certain subject areas, such as science, are highly dependent upon the use of visual simulations to demonstrate processes and systems. The humanities, especially art, history, and literature, can be greatly enhanced by rich digital images as well. Course/learning management systems ( CMS / LMS ) such as Blackboard, Canvas, or Moodle provide basic content delivery mechanisms for blended learning and easily handle the delivery of a variety of media including text, video, and audio. Games have also evolved and now play a larger role in instructional content. In providing and presenting content, the Blending with Pedagogical Purpose model suggests that multiple technologies and media be utilized.

The Blending with Pedagogical Purpose model posits that instruction is not simply about learning content or a skill but also supports students socially and emotionally . As noted, constructivists view teaching and learning as inherently social activities. The physical presence of a teacher or tutor, in addition to providing instruction, is comforting and familiar. While perhaps more traditionally recognized as critical for K-12 students, social and emotional development must be acknowledged as important to education at all levels. Faculty members who have taught graduate courses know that students, even at this advanced level, frequently need someone with whom to speak, whether to help understand a complex concept or to provide advice about career and professional opportunities. While fully online courses and programs have evolved to the point where faculty members can provide some social and emotional support where possible and appropriate, in blended courses and programs this is more frequently provided in a face-to-face mode.

Dialectics or questioning is an important activity that allows faculty members to probe what students know and to help refine their knowledge. The Socratic Method remains one of the major techniques used in instruction, and many successful teachers are proud of their ability to stimulate discussion by asking the “right” questions to help students think critically about a topic or issue. In many cases, these questions serve to refine and narrow a discussion to very specific “points” or aspects of the topic at hand, and are not meant to be open-ended activities. For dialectic and questioning activities, a simple-to-use, threaded electronic discussion board or forum such as VoiceThread is an effective approach. A well-organized discussion board activity generally seeks to present a topic or issue and have students respond to questions and provide their own perspectives, while evaluating and responding to the opinions of others. The simple, direct visual of the “thread” also allows students to see how the entire discussion or lesson has evolved. In sum, for instructors who want to focus attention and dialogue on a specific topic, the main activity for many online courses has been, and continues to be, the electronic discussion board.

Reflection can be incorporated as a powerful pedagogical strategy under the right circumstances. There is an extensive body of scholarship on the “reflective teacher” and the “reflective learner” dating from the early 20th century (Dewey (1916), Schon (1983)). While reflection can be a deeply personal activity, the ability to share one’s reflections with others can be beneficial. Pedagogical activities that require students to reflect on what they learn and to share their reflections with their teachers and fellow students extend and enrich reflection. Blogs and blogging, whether as group exercises or for individual journaling activities, have evolved into appropriate tools for student reflection and other aspects of course activities.

Collaborative learning has evolved over decades. In face-to-face classes, group work grew in popularity and became commonplace in many course activities. Many professional programs, such as business administration, education, health science, and social work, rely heavily on collaborative learning as a technique for group problem solving. In the past, the logistics and time needed for effective collaboration in face-to-face classes were sometimes problematic. Now, email, mobile technology, and other forms of electronic communication alleviate some of these logistical issues. Wikis, especially, have grown in popularity and are becoming a staple in group projects and writing assignments. They are seen as important vehicles for creating knowledge and content, as well as for generating peer-review and evaluation (Fredericksen, 2015). Unlike face-to-face group work that typically ended up on the instructor’s desk when delivered in paper form, wikis allow students to generate content that can be shared with others during and beyond the end of a semester. Papers and projects developed through wikis can pass seamlessly from one group to another and from one class to another.

Evaluation of learning is perhaps the most important component of the model. CMS s/ LMS s and other online tools and platforms provide a number of mechanisms to assist in this area. Papers, tests, assignments, and portfolios are among the major methods used for student learning assessment, and are easily done electronically. Essays and term projects pass back and forth between teacher and student without the need for paper. Oral classroom presentations are giving way to YouTube videos and podcasts. The portfolio is evolving into an electronic multimedia presentation of images, video, and audio that goes far beyond the three-inch, paper-filled binder. Weekly class discussions on discussion boards or blogs provide the instructor with an electronic record that can be reviewed over and over again to examine how students have participated and progressed over time. They are also most helpful to instructors to assess their own teaching and to review what worked and what did not work in a class. Increasingly, learning analytics are seen as the mechanisms for mining this trove of data to improve learning and teaching. In sum, online technology allows for a more seamless sharing of evaluation and assessment activities, and provides a permanent, accessible record for students and teachers.

The six components of the model described above form an integrated community of learning in which rich interaction, whether online or face-to-face, can be provided and blended across all modules. Furthermore, not every course must incorporate all of the activities and approaches of the model. The pedagogical objectives of a course should drive the activities and, hence, the approaches. For example, not every course needs to require collaborative learning or dialectic questioning. In addition to individual courses, faculty and instructional designers might consider examining an entire academic program to determine which components of the model best fit with overall programmatic goals and objectives. Here, the concept of learning extends beyond the course to the larger academic program where activities might integrate across courses. For example, some MBA programs enroll a cohort of students into three courses in the same semester but require that one or more assignments or projects be common to all three courses.

The critical question for our discussion, however, is whether this Blending with Pedagogical Purpose model can be modified or enlarged to be considered a model for online education in general. By incorporating several of the components from other theories and models discussed earlier in this article, this is a possibility. Figure 5.8 presents a Multimodal Model for Online Education that expands on the Blending with Purpose approach and adds several new components from Anderson and others, namely, community, interaction, and self-paced, independent instruction.

Multimodal model for online education

First, the concept of a learning community as promoted by Garrison, Anderson, and Archer (2000) and Wenger and Lave (1991) is emphasized. A course is conceived of as a learning community. This community can be extended to a larger academic program. Second, it is understood that interaction is a basic characteristic of the community and permeates the model to the extent needed. Third, and perhaps the most important revision, is the addition of the self-study/independent learning module that Anderson emphasized as incompatible with any of the community-based models. In this model, self-study/independent learning can be integrated with other modules as needed or as the primary mode of instructional delivery. Adaptive learning software, an increasingly popular form of self-study, can stand alone or be integrated into other components of the model. The latter is commonly done at the secondary school level where adaptive software programs are used primarily in stand-alone mode with teachers available to act as tutors when needed. Adaptive software is also integrated into traditional, face-to-face classes, such as science, where it is possible to have the instructor assign a lab activity that uses adaptive learning simulation software.

This Multimodal Model of Online Education attempts to address the issues that others, particularly Terry Anderson, have raised regarding elements that might be needed for an integrated or unified theory or model for online education. Whether or not this model finds acceptance is not yet clear. It is hoped that this article might serve as a vehicle for a critical examination of the model.

10 Applying the Integrated Model

To provide a clearer understanding of the integrated model, several examples of its application follow. Figure 5.9 provides an example of the model as a representation of a self-paced, fully online course. The three major components [in green] for this course are: content as provided on an LMS / CMS , a self-paced study module, and assessment/evaluation. Other components of the model, such as a blog or discussion board to allow interaction among students, could be included but are not necessarily needed. This example is most appropriate for online programs that have rolling admissions and students are not limited by a semester schedule. Students proceed at their own pace to complete the course as is typical in some distance education programs. This example is scalable and can be used for large numbers of students.

Figure 5.10 provides an example of another course that is primarily a self-paced, online course similar to that described in Figure 5.9 but is designed to have a teacher or tutor available as needed. A discussion board is also included to allow for ongoing interaction among students and teacher. This course would follow a semester schedule and would have a standard class size although most of the instruction would be provided by the self-paced study module. A standard course organization would be used, with a teacher or tutor assigned to guide and assist with instruction. The teacher or tutor could help students struggling with any of the self-paced material. This type of course is increasingly common in secondary schools, such as in credit-recovery courses.

Example of a distance education course

Example of a modified distance education course.

Figure 5.11 provides an example of a teacher-led, fully online course. Presentation of the course content is provided by a LMS or CMS along with other media and is used as needed by the teacher. The discussion board, blog, and wiki provide facilities for interaction among teachers and students, students and students, and students and content. In this course, the teacher could direct students to watch a fifteen-minute lecture available in the LMS database and then ask students to respond to a series of questions on the discussion board. Student responses can then be used as the basis for an interactive discussion board activity among students, guided by the teacher. The model also provides for reflection and collaborative activities.

Example of a teacher-led fully online course

Figure 5.12 provides an example of a blended course with instruction provided primarily by a teacher. The other modules are used to extend and enrich instruction. The teacher is the major guide for instruction and would be supplemented by content as needed by a LMS / CMS . The course would meet in a face-to-face classroom although some instructional activity would also be conducted online, either on a discussion board, a blog, or a collaborative wiki. The teacher would establish beforehand portions of the course that would meet in the face-to-face and online modes.

Example of a mainstream blended course

11 attributes and limitations of the multimodal model.

The proposed Multimodal Model for Online Education includes many of the major attributes of other learning and online education theories and models. For example, behaviorists will find elements of self-study and independent learning in adaptive software. Cognitivists might appreciate reflection and dialectic questioning as important elements of the model. Social constructivists will welcome the emphasis on community and interaction throughout the model. Connectivists might value the collaboration and the possibility of student-generated content. Perhaps the most significant element of the model is its flexibility and ability to expand as new learning approaches, perhaps spurred by advances in technology, evolve.

The model is not without limitations. Learning theories can be approached through a number of perspectives and disciplines. Behavioral psychologists, cognitive psychologists, sociologists, and teacher educators might emphasize the need for deeper considerations of their perspectives for an online learning theory. The multimodal model here represents an integrated composite of several such perspectives but is essentially a pedagogical model and, therefore, may have greater appeal to instructional designers, faculty, and others who focus on learning objectives.

12 Conclusion

In this article, a number of major theories related to technology were presented, beginning with a review of major theories associated with learning. One critical question concerned whether an integrated or unified theory of online education could be developed. The work of Terry Anderson was highlighted. The article proposed an integrated model that described the phenomenon of pedagogically driven online education. Key to this model is the assumption that online education has evolved as a subset of learning in general rather than a subset of distance learning. As blended learning, which combines face-to-face and online instruction, evolves into the dominant form of instruction throughout all levels of education, it serves as the basis for an integrated model. It is likely that, in the not-too-distant future, all courses and programs will have some online learning components, as suggested in this integrated model.

• – This chapter does not address change theory, an important, practical theory for any leader of Distance Learning. Go online to explore how to facilitate change. Identify at least three strategies that you would apply as a distance learning leader.
• – Given Siemen’s eight Principles of Connectivism and Harasim’s three phases of Online Collaborative Learning, describe an activity incorporating these ideas through which you could lead faculty members toward using the Internet constructively in their learning.
• – Using Picciano’s Multimodel Model for Online Education, analyze one or more courses that you have taken or that you have taught by identifying the components that apply to that course or courses.

Acknowledgment

This chapter was previously published and is used here with permission from the author and publisher: Picciano, A. G. (2017). Theories and frameworks for online education: Seeking an integrated model. Online Learning , 21 (3), 166–190. doi: 10.24059/olj.v21i3.1225

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Online learning.

• Lisa Marie Blaschke Lisa Marie Blaschke Carl von Ossietzky University
• , and  Svenja Bedenlier Svenja Bedenlier Friedrich-Alexander-University Erlangen-Nürnberg
• https://doi.org/10.1093/acrefore/9780190264093.013.674
• Published online: 30 April 2020

With the ubiquity of the Internet and the pedagogical opportunities that digital media afford for education on all levels, online learning constitutes a form of education that accommodates learners’ individual needs beyond traditional face-to-face instruction, allowing it to occur with the student physically separated from the instructor. Online learning and distance education have entered into the mainstream of educational provision at of most of the 21st century’s higher education institutions.

With its consequent focus on the learner and elements of course accessibility and flexibility and learner collaboration, online learning renegotiates the meaning of teaching and learning, positioning students at the heart of the process and requiring new competencies for successful online learners as well as instructors. New teaching and learning strategies, support structures, and services are being developed and implemented and often require system-wide changes within higher education institutions.

Drawing on central elements from the field of distance education, both in practice and in its theoretical foundations, online learning makes use of new affordances of a variety of information and communication technologies—ranging from multimedia learning objects to social and collaborative media and entire virtual learning environments. Fundamental learning theories are being revisited and discussed in the context of online learning, leaving room for their further development and application in the digital age.

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Students’ online learning adaptability and their continuous usage intention across different disciplines

• Zheng Li 1 ,
• Xiaodong Lou 2 ,
• Minwei Chen 3 ,
• Siyu Li 1 ,
• Cixian Lv 4 ,
• Shuting Song 4 &
• Linlin Li 4  

Humanities and Social Sciences Communications volume  10 , Article number:  838 ( 2023 ) Cite this article

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Online learning, as a pivotal element in modern education, is introducing fresh demands and challenges to the established teaching norms across various subjects. The adaptability of students to online learning and their sustained willingness to engage with it constitute two pivotal factors influencing the effective operation of online education systems. The dynamic relationship between these aspects may manifest unique traits within different academic disciplines, yet comprehensive research in this area remains notably scarce. In light of this, this study constructs an Adaptive Structural Learning and Technology Acceptance Model (ASL-TAM) with satisfaction towards online teaching as the mediating variable to investigate the impact and mechanism of online learning adaptivity on continuous usage intention for students from different disciplines. A total of 11,832 undergraduate students from 334 universities in 12 disciplinary categories in mainland China were selected, and structural equation modeling was used for analysis. The results showed that the ASL-TAM model could be fitted for all 12 disciplines. The perceived ease of use, perceived usefulness, and system environment adaptability dimensions of online learning adaptivity significantly and positively affect satisfaction towards online teaching and continuous usage intention. Satisfaction towards online teaching partially mediates the relationship between online learning adaptivity and continuous usage intention. There were significant differences in the results of the single-factor analysis of the observed variables for the 12 disciplines, and the path coefficients in the ASL-TAM model fitted for each discipline were also significantly different. Compared to the six disciplines under the science, technology, engineering, and mathematics (STEM) category, six disciplines under the humanities category exhibited more significant internal differences in the results of the single-factor analysis of perceived usefulness and the path coefficients for satisfaction towards online teaching. This research seeks to bridge existing research gaps and provide novel guidance and recommendations for the personalized design and distinctive implementation of online learning platforms and courses across various academic disciplines.

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Introduction.

With the rapid development of information technology, online learning has become an integral part of modern education. China possesses the largest scale of higher education system and online learning course system globally (National Bureau of Statistics ( 2020 )). However, despite the widespread adoption of online learning platforms, there remain controversies surrounding students’ engagement, satisfaction, and willingness to continue using them. Therefore, researching how to enhance students’ willingness to persist in using online learning platforms is of paramount importance for the development and promotion of online learning. In recent years, scholars have increasingly focused on the factor of students’ online learning adaptability when studying the effectiveness of online learning for students and their willingness to continue using online platforms.

Prior studies have indicated that the overall level of adaptability to online learning among college students is relatively low (Luo, Huang ( 2012 )), and adaptability often becomes a critical factor determining the quality of learning and academic assessment in an online learning environment (D’errico et al., ( 2018 )). However, there is currently insufficient research evidence to fully understand the specific mechanisms through which online learning adaptability affects willingness to persist in using online platforms, necessitating further empirical research.

Moreover, given the extensive use and profound influence of online learning technologies in diverse academic fields (Chikwa et al., ( 2015 )), alongside the marked disparities in online learning outcomes across these disciplines (Ieta et al., ( 2011 )), delving into the intricate interplay between students’ online learning adaptability and their inclination to persist in using these tools across various domains becomes particularly instructive. It can provide valuable insights for crafting precise and efficacious online learning strategies and pedagogical models aimed at enhancing student learning outcomes and bolstering students’ satisfaction with online education.

As such, this study aims to investigate the impact of online learning adaptability on the willingness to persist in using online platforms among students from different disciplines while exploring the potential mediating effect of their satisfaction towards online teaching. This study randomly selected 11,832 valid samples from 256,504 students attending online learning in 12 disciplines across 334 universities in mainland China. Using structural equation modeling, the study analyzed the comprehensive impact of students’ online learning adaptability on their continued use intention of online learning. The study also analyzed the possible mediating effects of satisfaction towards online teaching among the 12 disciplinary categories in the “Degree Granting and Talent Training Discipline Catalog” issued by the Ministry of Education of China.

Theoretical foundation and research hypotheses

Adaptive online learning and continuance intention and their influencing factors.

Continuous usage intention is originated from the tracking and evaluation of the continuous use of software programs. It refers to the user’s decision to continue using a software application and the frequency of use based on the overall perception of the application. Continuous usage intention is one of the most important user indicators for judging the software system’s life cycle. This study applies this factor in the context of online learning research, and forms the concept of “continuance intention of online learning,” which is defined as learners’ intentions to continue choosing online learning as the primary learning method. This study seeks to determine whether students are willing to continue using this type of learning after a certain period of time.

In contrast to Daumiller et al. ( 2021 ), who suggest that teachers’ goals and attitudes have a critical impact on students’ continuance intention to use online learning, Yao et al. ( 2022 ) believe that the key factor affecting continued use of online learning is students’ self-awareness, which is closely related to their adaptability to online learning. Online learning adaptability refers to students’ ability to adapt to the learning environment by adjusting their learning strategies and adopting adaptive behaviors when using online learning platforms or systems. In the 1980s, Davis ( 1986 ) drew on the Theory of Reasoned Action to propose the Technology Acceptance Model (TAM). TAM is primarily used to predict the extent to which individuals are inclined to accept, use, or reject new information technologies (Rogers, 2005 ). Given that online learning adaptability can help students overcome difficulties and challenges in the learning process, increasing their acceptance and depth of use of online learning, students’ adaptability to online learning platforms or systems is likely to be one of the important factors influencing their decision to continue using online platforms.

Online learning adaptability is a complex, multidimensional concept. Generally, it is considered the ability of students to adjust their learning strategies, behaviors, attitudes, goal setting, and resource utilization to adapt to new learning conditions and requirements (Kizilcec et al., 2015 ). This includes adaptability in areas such as technical proficiency, self-management skills, and information literacy. Among these, the adaptability of university students to online learning primarily depends on their familiarity with the technology tools they use. Therefore, mastering online learning platforms, social media, and digital tools can enhance students’ adaptability to online learning (Selwyn, 2011 ). Additionally, in terms of instructional design, the design of online courses has a significant impact on students’ adaptability. Clear learning objectives, organized content, and diverse teaching methods contribute to improving students’ adaptability (Picciano, 2017 ). Providing effective technical support and assistance channels can alleviate students’ technological difficulties and enhance their adaptability to online learning (Johnson & Adams, 2011 ).

In analyzing the issues of online learning adaptability and acceptance, TAM provides several foundational factors, such as perceived ease of use, perceived usefulness, satisfaction, and self-efficacy (Cakır, Solak ( 2015 )). Perceived usefulness and perceived ease of use are generally considered the two most essential variables (Martins et al., 2014 ). Perceived usefulness refers to the degree to which users believe that using a particular information technology enhances their work efficiency, while perceived ease of use refers to users’ perception of how easy it is to operate a specific information technology (Davis, 1989 ). Alharbi and Drew ( 2014 ) argue that perceived ease of use and perceived usefulness in the TAM model significantly positively influence students’ intentions to use online learning. Therefore, this study proposes the following hypotheses:

H1a: The perceived usefulness dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

H1b: The perceived ease of use dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Apart from perceived ease of use and perceived usefulness, there is still no consensus on other important factors influencing continuance intention, especially regarding the strength and mechanisms of different factors (Joo et al., 2011 ). Liu et al. ( 2010 ) suggests that reasonable external extension variables can effectively predict users’ intentions to use online learning. Bazelais et al. ( 2018 ) and Xu, Lv ( 2022 ) also propose considering the additional effects of external influencing variables in the study of continuance intention. As a frontier and hot topic in online learning research (Jovanovic, Jovanovic ( 2015 )), the theory of Adaptive Learning Systems (ALS) from cognitive psychology proposes the concept of “human-machine interaction adaptability,” which includes two aspects: human adaptation to technology and technology adaptation to humans. The latter relies on the “learner model” to automatically analyze learners’ cognitive levels and learning styles, and then feedback to the former to enhance learners’ learning progress and effectiveness (Retalis, Papasalouros ( 2005 )). Social Cognitive Theory also suggests a similar viewpoint, indicating that students’ adaptability is largely influenced by multiple social contexts. A substantial amount of research on ALS also demonstrates that ALS, as a scientific learning medium, can more actively meet students’ learning needs (How, Hung ( 2019 )), help correct the learning paths generated by students’ autonomous learning habits (Nihad et al., ( 2017 )), and effectively improve students’ learning adaptability (Zulfiani et al., ( 2018 )). This study believes that online learning adaptability is a comprehensive, two-way process for students to adapt to changes in the learning environment through self-perception and for software systems to adapt to user needs systematically. It includes three variables: perceived ease of use, perceived usefulness, and system environment adaptability, with the latter referring to the functional adaptability of learning software systems to different learning styles of learners. Therefore, this study proposes the following hypothesis:

H1c: The system environment adaptation dimension of online learning adaptability has a positive significant impact on students’ continued usage intention.

Satisfaction towards online teaching and its possible mediating role

Prior research has suggested that satisfaction towards online teaching and perceived usefulness are considered core components in evaluating the effectiveness of online learning (Menon, Seow ( 2021 )), as they relate to the quality of online courses and students’ performance (Kuo et al., 2014 ). Scholars attach great importance to the research on the relationship between students’ satisfaction towards online teaching and their continued usage intention, with satisfaction being considered a key element affecting students’ continued usage intention and behavior (Lee, 2010 ).

Among the potential factors contributing to positive adaptability in online learning, perceived usefulness and perceived ease of use are recognized as two significant factors affecting satisfaction (Huang, 2020 ). Additionally, factors influencing satisfaction can indirectly impact the intention to continue using the system (Bhattacherjee, 2001 ). Furthermore, online educational platforms with robust system adaptability can provide a more stable network connection, higher-quality learning resources, and a more diverse array of learning pathways. Moreover, they can deliver personalized learning support and teaching resources tailored to individual student needs and learning characteristics. This assists students in overcoming learning challenges and enhances teaching effectiveness, ultimately leading to greater teaching satisfaction. Notably, technological innovations introduced by ALS effectively enhance learners’ perceived quality and have a positive indirect influence on teaching satisfaction (Janati et al., ( 2018 )). Therefore, the following hypotheses are proposed:

H2a: The perceived usefulness dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2b: The perceived ease of use dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

H2c: The system environment adaptation dimension of online learning adaptability positively and significantly affects students’ satisfaction towards online teaching.

It is generally believed that students’ satisfaction towards online teaching can refer to the indicator system proposed by the research on satisfaction towards classroom teaching, comprehensively evaluating common teaching factors such as course design, learning objectives, teaching methods, teacher qualifications, and interactive experiences. Palmer, Holt ( 2010 ) believe that the research on students’ satisfaction towards online teaching should pay more attention to the unique factors of the online teaching environment, such as teaching interactivity, technical proficiency, and online self-assessment. Bolliger and Wasilik ( 2009 ) also believes that we should start from the key participants in the online environment, focusing on the impact of various aspects such as teachers’ information technology application, students’ communication level, and school policy and logistical support. Kurucay and Inan ( 2017 ) opine that the key factor influencing online learning effectiveness is the interaction between learners. Regarding the main factors influencing learners’ satisfaction towards online teaching, Kranzow ( 2013 ) believe that the essential factors are related to teacher’s online course design level and the ability to respond to student needs in a timely manner. Hogan and McKnight ( 2007 ) believe that factors such as the teaching environment and technical support are the main reasons for influencing satisfaction towards online teaching. In addition, there are significant differences in the predicting factors for the acceptance of online learning and satisfaction towards online teaching among university students from different countries (Piccoli et al., 2001 ). Based on the above research, this study will further analyze the factors influencing learners’ satisfaction towards online teaching in the online learning environment, and propose the following hypothesis:

H3: Students’ satisfaction towards online teaching positively affects their continued usage intention.

Previous studies have shown that students’ satisfaction towards online teaching is likely to be influenced by their learning adaptability, and at the same time affects their intention to continue attending online learning (Waheed, 2010 ). Therefore, students’ satisfaction towards online teaching may play a special mediating role between students’ learning adaptability and their continuance intention. Yeung and Jordan ( 2007 ) found that factors such as perceived usefulness, perceived ease of use, and service quality evaluation that affect online learning satisfaction also have a positive impact on students’ continuance intention. Young ( 2013 ) reached similar conclusions and believed that students’ satisfaction towards online teaching plays a mediating role in the process of affecting their continuance intention. However, there are also different views about this topic. For example, Troshani et al. ( 2011 ) found that although perceived ease of use has a significant impact on learners’ usage satisfaction, it does not have a significant impact on their continuance intention. Therefore, the mediating effect of learning adaptability on learners’ continuance intention may be extremely important and needs to be verified through empirical research. Therefore, this study proposes that students’ satisfaction towards online teaching plays a mediating role between their online learning adaptability and continued usage intention. The specific hypotheses are as follows.

H4a: Students’ satisfaction towards online teaching plays a mediating role between perceived usefulness and their continued usage intention.

H4b: Students’ satisfaction towards online teaching plays a mediating role between perceived ease of use and their continued usage intention.

H4c: Students’ satisfaction towards online teaching plays a mediating role between system environment adaptability and their continued usage intention.

Designing the model framework

As mentioned earlier, it is feasible to use the TAM model to study the sustained usage intention of online learning, and its explanatory power has been verified by empirical studies (Dziuban et al., 2013 ). However, with the increasing complexity of the online environment, the traditional TAM model may encounter issues with low reliability and validity in explaining complex user environments. Therefore, the academic community has been continuously selecting, combining, and adjusting the basic components of the TAM model. Davis et al. ( 1992 ) pointed out that when using TAM theory, multiple external variables, including intrinsic motivation, should be considered, as they may have complex effects on endogenous variables and behavioral intentions. Farahat ( 2012 ) found that, in addition to perceived usefulness and perceived ease of use, student attitudes and social influences in online learning are also important factors that influence students’ willingness to engage in online learning. Therefore, based on the Technology Acceptance Model (TAM) and the Adaptive Structural Learning Model (ALS), this study combines them to construct the Adaptive Learning and Technology Acceptance Model (ASL-TAM model; see Fig. 1 ) as follows:

In ASL-TAM model, online learning adaptability consists of three factors, which are hypothesized to predict continued usage intention and satisfaction towards online teaching.

Methodology

Data source.

The data for this study were collected from an online learning survey conducted by a Teacher Development Centre of a public university (IRB No. NB-HEC-20200328L) in mainland China from 2020 to 2021. The survey was distributed to students through the academic affairs offices of various schools. Additionally, two lie-detection questions were included in the questionnaire to ensure the validity and reliability of the data. Each student account could only save one survey form. In other words, if the same account answered multiple times, the results of the last response would automatically overwrite the previous ones. A total of 256,504 data sets were collected from 334 universities. Among the surveyed students, there were 110,411 males (43%) and 146,093 females (57%). In terms of geographical distribution, 110,919 students (43.2%) were from the eastern region of China, 106,007 (41.3%) were from the central region, and 38,847 (15.1%) were from the western region. The surveyed students were also classified into different academic disciplines, including 11,086 in philosophy, 20,953 in economics, 7420 in law, 17,100 in education, 24,658 in literature, 1201 in history, 29,517 in natural science, 76,301 in engineering, 5295 in agriculture, 11,161 in medicine, 24,583 in management, and 27,229 in arts. A sample of 1000 student questionnaires was randomly selected from each academic discipline, resulting in a total of 12,000 data sets. The sample was cleaned based on criteria such as lie-detection questions, response times (data below 5 min or above 20 min were removed based on the statistical “3σ rule”), age (data below 15 years old or above 25 years old were removed based on the statistical “3σ rule”), school names (data with randomly filled school names were removed), and whether online learning was used (data indicating no usage were removed). In total, 162 samples were cleaned, resulting in 11,832 valid samples (with 986 for each of the 12 academic disciplines).

Instrumentation

This study was conceptualized based on TAM from the theory of rational behavior and the ALS theory from cognitive psychology. These theories were employed to investigate the underlying mechanisms of the impact of online learning adaptability on users’ continuance intention. In this regard, we consulted the research findings of scholars such as Davis ( 1993 ), Igbaria ( 1990 ), Ajzen & Fishbein ( 1980 ), Chen and Tseng ( 2012 ), among others. The questionnaire consisted of 33 items measuring five variables (see Table S1 for the complete questionnaire): perceived usefulness (11 items), perceived ease of use (3 items), adjustment to system environments (10 items), satisfaction of teaching (7 items), and continuance intention (2 items). The overall reliability of the questionnaire was tested using the Cronbach’s alpha coefficient (0.924), KMO (0.937), and Bartlett’s sphericity test ( p  < 0.001) in SPSS 25.0 software, indicating that the questionnaire data were reliable and suitable for exploratory factor analysis (EFA). Three principal components were extracted for perceived usefulness (PU): teaching resources (PU_TR), classroom teaching (PU_CT), and teaching evaluation (PU_TE). Three principal components were also extracted for perceived ease of use (PEU): technical training (PEU_TT), pedagogical training (PEU_PT), and proficiency levels (PEU_PL). Three principal components were extracted for system environment adaptation (SEA): technical service (SEA_TSER), teaching support (SEA_TSUP), and policy support (SEA_PS). Three principal components were extracted for satisfaction with online teaching (ST): effectiveness of teaching (ST_TE), teaching experience (ST_TEXP), and learning outcomes (ST_LO). Two principal components were extracted for continuance intention (CIN): online mode (CIN_ON) and blended mode (CIN_BL). Perceived usefulness, perceived ease of use, and system environment adaptation were combined to form the independent variable “adaptive structural learning (ASL)” in this study, while satisfaction towards online teaching was the hypothesized mediating variable and continuance intention was the dependent variable. The academic disciplines were treated as control variables. The perceived usefulness and perceived ease of use scales were adapted from Davis ( 1993 ), the system environment adaptation scale was adapted from Igbaria ( 1990 ), the satisfaction towards online teaching scale was adapted from Ajzen and Fishbein ( 1980 ), and the continuance intention scale was adapted from Chen and Tseng ( 2012 ).

Research method

Descriptive statistics were conducted on the data of 12 disciplines using SPSS 25.0 software, and model construction, model revision, and model interpretation were carried out using AMOS 24.0.

Reliability analysis

Reliability analysis was conducted on the 14 latent variables across the 12 disciplines using SPSS 25.0 software (see Table 1 for results). The results showed that the alpha values of the observation variables based on standardized items were all greater than or equal to 0.9, indicating that the questionnaire of the 12 disciplines had high reliability. During reliability analysis, the scores of the latent variables calculated using the mean method also had considerable reliability, indicating excellent data reliability. The data of the 12 disciplines were suitable for further structural model testing.

Common method bias (CMB) test

The data used in this study were collected through self-reporting methods on the internet, which may have CMB. Before formal data analysis, a Harman single-factor test was conducted to examine common method bias. First, exploratory factor analysis (unrotated) was performed using SPSS 25.0 software. The results showed that the first principal component accounted for 29.21% of the variance, which did not meet the 40% threshold.

One-way ANOVA of disciplinary variables

One-way ANOVA analysis was conducted on the observation variables of 12 disciplines. According to the results in Table 2 , if the 12 disciplines are viewed as a whole, the evaluation of perceived ease of use (3.62) is higher than system environment adaptation (3.60) and perceived usefulness (3.47). The satisfaction towards online teaching (3.47) is higher than continuous usage intention (3.44). Perceived usefulness is the main weak link of online learning adaptability, and the main observation variable that causes the low value of perceived usefulness is teaching evaluation (3.26). The lowest discipline evaluation value comes from philosophy (3.41). The observation variable with the lowest evaluation value in perceived ease of use is technical training (3.58), and the observation variable with the lowest evaluation value in system environment adaptation is technical service (3.53). The observation variable with the lowest evaluation value in the satisfaction towards online teaching is effectiveness of teaching (3.28). All 14 observation variables of the 12 disciplines showed significant inter-group differences ( p  < 0.001), indicating that there were general differences in the evaluation outcomes among the observation variables of different disciplines.

Correlation analysis among variables

To explore the relationships between the variables, a correlation analysis was performed. As shown in Table 3 , there were significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation. There were also significant positive correlations ( p  < 0.001) between the variables of perceived usefulness, perceived ease of use, and system environment adaptation with the mediating variables of satisfaction towards online teaching and continued usage intention. Additionally, there was a significant positive correlation ( p  < 0.001) between satisfaction towards online teaching and continued usage intention.

Model construction and fitting

Based on the ASL-TAM model developed in Fig. 1 , a structural equation model was constructed using AMOS 24.0 software, and the initial model was estimated using maximum likelihood. Taking the subject of physics as an example, the results of the initial model fit showed that the correction index MI value of the residual path [e2 < -->e3] was relatively large. Therefore, the initial model was corrected by adding the [e2 < -->e3] residual path, and all path p -values were less than 0.05 after the correction, indicating statistical significance. The fitted model is shown in Fig. 2 .

The validated ASL-TAM model for the subject of physics demonstrated good fit, with most hypotheses being substantiated.

The fitted model for the subject of physics showed good results. The same fitting method was used for the other 11 subjects, and the results showed that all 12 models could be fitted, and the 12 fitting goodness-of-fit indices were within the standard range. Therefore, the ASL-TAM model can be used for relevant evaluation and prediction work (see Table 4 for goodness-of-fit indices).

Path analysis results of fitted models

The path coefficients of the structural equation can reflect the mutual relationships between latent variables and between latent variables and observed variables. The path coefficients between variables after the fitting of the 12 subjects are shown in Table 5 . First, the ASL-TAM models of all 12 subjects can achieve overall convergence. The path coefficients of satisfaction towards online teaching (ST) on continuous usage intention (CIN) are all significant in all 12 subjects, verifying research hypothesis H3. Second, the three paths “perceived ease of use (PEU) → continuous usage intention (CIN)”, “perceived usefulness (PU) → continuous usage intention (CIN)”, and “system environment adaptation (SEA) → continuous usage intention (CIN)” all display significant path coefficients and can be fitted into the ASL-TAM model, indicating that online learning adaptability and its three dimensions all have a significant positive impact on continuous usage intention (CIN), substantiating research hypotheses H1, H1a, H1b, and H1c. Third, the three paths “perceived ease of use (PEU) → satisfaction towards online teaching (ST)”, “perceived usefulness (PU) → satisfaction towards online teaching (ST)”, and “system environment adaptation (SEA) → satisfaction towards online teaching (ST)” all display significant path coefficients, indicating that online learning adaptability and its three dimensions all have a significant positive impact on satisfaction towards online teaching (ST), verifying research hypotheses H2, H2a, H2b, and H2c. Additionally, the path “Satisfaction towards online teaching (ST) → continuous usage intention (CIN)” is displayed with a significant path coefficient in all 12 subjects, indicating that “satisfaction towards online teaching (ST)” has a partial mediating effect between “perceived ease of use (PEU)”, “perceived usefulness (PU)”, “system environment adaptation (SEA)” and “continuous usage intention (CIN)”, verifying research hypotheses H4, H4a, H4b, and H4c.

This study confirms the positive impact of online learning adaptability on users’ intention to continue using the platform. This aligns with previous research findings that students’ adaptation to a course significantly affects their learning outcomes (Manwaring et al., 2017 ). Unlike most studies that only focus on students’ one-way adaptation to the teaching system, this study confirms that both students’ “perceived adaptation” to the system and the system’s “adaptive needs” to the students are equally important and should be considered as a whole. When students’ perceived position in the system matches the target characteristics predicted by the system, they will rate the teaching activities higher (Bretschneider et al., 2012 ).

This study also confirms the positive impact of online learning adaptability on satisfaction towards online teaching, which is in line with previous research that adaptability is an important indicator of students’ learning satisfaction, perceived utility, and intention to continue learning (Machado, Meirelles ( 2015 )). Therefore, adaptability should be the logical starting point for designing online learning systems. At the same time, enhancing the intelligence perception of “human-computer interaction” and improving the teaching adaptivity of “teacher-student interaction” are important directions for enhancing users’ intention to continue using online learning and improving the overall quality of online learning.

This study also confirms the positive impact of satisfaction towards online teaching on users’ intention to continue using the platform, and the TAM model is applicable in evaluating satisfaction and intention to continue using in 12 subject areas. The adaptive structural learning and technology acceptance model fit successfully in all 12 subject areas. This confirms that the TAM model can be used to explain the factors that influence learners’ acceptance of online learning (Venkatesh, Davis ( 2000 )), and the core structure of TAM has a significant impact on users’ intention to continue using (Natasia et al., 2022 ).

Furthermore, this study confirms that satisfaction towards online teaching partially mediates the relationship between online learning adaptability and users’ intention to continue using the platform. The ASL-TAM model developed in this study reveals that there are expression differences in the factors that affect satisfaction towards online teaching and users’ intention to continue using in the 12 subject areas, and the ASL-TAM model can explore the deep path reasons for the expression differences in the factors affecting users’ intention to continue using (Al-Azawei, Lundqvist ( 2015 )), and then analyze the educational goals and methods paths for implementing online learning in different subjects.

This study has three contributions. First, the study found that perceived usefulness (PU) (3.47) was lower than system environment adaptation (SEA) (3.60) and perceived ease of use (PEU) (3.62). The continuous usage intention (CIN) (3.44) was lower than satisfaction towards online teaching (ST) (3.47). The main observed variables leading to a low evaluation of perceived usefulness (PU) were teaching evaluation (PU_TE) (3.26) while the lowest evaluated variable in perceived ease of use (PEU) was technology training (PEU_TT) (3.58). In system environment adaptation (SEA), the lowest evaluated variable was technical service (SEA_TSER) (3.53) while the lowest evaluated variable in satisfaction towards online teaching (ST) was teaching effectiveness (ST_TE) (3.28). This indicates that online education in mainland China is still in the early stage of hardware facilities configuration and teaching technology training. The continuous usage intention (CIN) is generally weak, possibly due to the weak links in the early adaptation to online learning, which affects the evaluation of satisfaction towards online teaching (ST), leading to a weaker overall continuous usage intention (CIN). Online learning needs more specific and effective project support (Ramadhan et al., 2021 ).

Second, the study confirms that satisfaction towards online teaching (ST) plays a partial or complete mediating effect between perceived ease of use (PEU), perceived usefulness (PU), system environment adaptation (SEA) and continuous usage intention (CIN), which confirms previous research conclusions. That is, user satisfaction is a key antecedent to influence user intention to continue use and behavior (Igbaria et al., 1997 ). There are many possible factors that influence continuous usage intention (CIN) of a teaching method, but among various factors, satisfaction towards online teaching (ST) of the student population is the “central factor”, especially for online education, learner satisfaction is considered a key factor for teaching success (Joo et al., 2011 ). It is also important to strengthen system environment adaptation (SEA) based on human-computer interaction, as online learning requires an attractive and motivational external environment (Agyeiwaah et al., 2022 ), and satisfaction may vary due to internet experience (Reed, 2001 ).

Thirdly, this study confirms the significant differences in satisfaction towards online teaching (ST) and continuous usage intention (CIN) between STEM and humanities disciplines. Influenced by the early college entrance examination system, China has conventionally classified disciplines into STEM and humanities, similar to the “arts” and “science” branches in the subject guidelines of Western universities. The classification not only affects the disciplines but also results in significant differences in academic literacy among students in different fields. This study found that compared to STEM disciplines (such as natural science, engineering, agriculture, medicine, and management), the six traditional humanities disciplines, namely philosophy, law, education, literature, history, and economics, showed extremely significant differences in perceived usefulness (PU), which may be due to the difference in teaching style between humanities and STEM (Tuimur et al., 2012 ) and the peer cultural influence within the humanities. A study of nearly 500,000 online courses in the state of Washington in the United States has similar conclusions that students face greater difficulties in online learning in fields like English and social sciences, possibly due to the existence of “negative peer effects” in the online courses of these disciplines (Lv et al., 2022 ).

Implications

In order to enhance the satisfaction towards online teaching and continued usage intention of online education, this study proposes the following suggestions:

From the perspective of cognitive psychology, the differences in online teaching among different disciplines are mainly manifested in various aspects such as the cognitive perspectives and learning habits of students with different disciplinary backgrounds. From the standpoint of educational technology theory, there is a need for continuous development of multidimensional and multilevel teaching systems to adapt to the knowledge structures, teaching principles, and curriculum characteristics of different disciplines. Furthermore, constructivist learning theory emphasizes that teachers should assist students in improving their learning adaptability more actively and in constructing knowledge and meaning more proactively. This study empirically validates the above viewpoints and provides new discoveries. Research shows that there are significant differences in satisfaction towards online teaching and continued usage intention in online learning among different subjects, so different online learning for different subjects should be implemented. On the one hand, the convergence of online learning in different subjects should be grasped, and a wide-caliber, widely applicable teaching platform carrier should be constructed to effectively integrate different subject knowledge into the virtual classroom knowledge situation, and better promote the integration of knowledge and skills. On the other hand, attention should be paid to the objective differences of different subjects, and an online education system reflecting the advantages of different subjects should be designed according to the teaching contents of different subjects.

From the perspective of practicality, it is necessary to pay close attention to the significant differences among various disciplines in terms of subject content and learning objectives, teaching methods and learning activities, assessment and feedback methods, as well as the roles of teachers and technological support. It is important to actively develop teaching methods that are tailored to different disciplines, especially in the case of experimental courses. Compared with traditional classroom education, the important breakthrough of online learning is the more convenient and timely teaching feedback. Future online learning systems should create adaptive learning environments based on the different characteristics of learners (Park and Lee, 2003 ), and accelerate the construction of adaptive learning systems for college students with different learning methods in different subjects, which is an effective solution to the conflict between diversified subject needs and static teaching resources, and an important way to resolve the contradiction between diversified student levels and limited teaching resources. For science and engineering subjects, attention should be paid to improving the external environment of online learning, actively improving online learning performance evaluation, promoting industry-university-research cooperation, promoting demand docking, resource sharing, and complementary advantages, promoting industry-education integration and industry-university co-construction, and achieving win-win results for teachers and students. For humanities subjects, the technical support for each link of online learning should be improved, and more humanistic care should be reflected in interactive teaching support. Through more social integration, knowledge exploration-based social consultation can be promoted.

In terms of the broader external educational environment and technological development trends, we should emphasize the opportunities for educational technology innovation and industry-education integration brought about by the differential development of online teaching in various disciplines. Clearly, the issue of disciplinary differences presents challenges in terms of teaching organization and operation, but it also promotes opportunities for personalized learning, collaborative teaching, and diversified assessment. China is already a major player in online education, but it is not yet a powerhouse in this field. To unleash the educational value of online learning and expand its innovative significance, online education, represented by flipped classrooms and MOOCs, not only provides new teaching methods and educational pathways, but also brings innovative educational ideas and paradigms. Therefore, online education needs to emphasize the re-examination of external contexts, overcome the mechanical thinking of “100% replication of classroom education,” and explore new teaching paths and operating modes, providing teachers and students with more novel teaching experiences and promoting the comprehensive improvement of their knowledge, abilities, and qualities.

Limitations and future research

This study has two limitations. Firstly, to increase the credibility of the research conclusions, we have tried to increase the sample size, resulting in a relatively large number of universities involved in the study. These universities may have differences in their discipline settings and standards, which may introduce some errors that need to be addressed in future research. Secondly, previous studies have shown that factors such as the location of the participants, the level of their universities, and their academic year may affect their satisfaction with teaching. We were unable to eliminate these possible interferences in this study and will improve this in future research.

Data availability

The data presented in this study are available on request from the corresponding author. According to the regulation of the Ethics Committee of Ningbo University, the data are not publicly available due to ethical reasons as they contain personally identifiable information.

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Acknowledgements

This research was funded by the National Social Science Foundation (Education) Project, “Research on the Path and Mechanism of Universities Promoting Rural Entrepreneurship Education under the Background of Rural Revitalization” (grant No. BIA200204).

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Li, Z., Lou, X., Chen, M. et al. Students’ online learning adaptability and their continuous usage intention across different disciplines. Humanit Soc Sci Commun 10 , 838 (2023). https://doi.org/10.1057/s41599-023-02376-5

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E-Learning Theory

E-learning theory is built on cognitive science principles that demonstrate how the use and design of educational technology can enhance effective learning (David, 2015; Wang 2012). The theory was developed from a set of principles created based on Cognitive Load Theory (Sweller, Van Merriënboer & Paas, 2019). According to David (2015), Cognitive Load Theory is “the amount of mental effort involved in working memory” (n.p.) during a task and can be categorized into germane, intrinsic, and extraneous effort. Since the working memory has limited capacity and the brain will suffer from overload if learners are presented with too much information, causing inefficient learning, it is essential to balance these three types of load to promote learning efficiency (Clark, Nguyen & Sweller, 2005). Based on this, Mayer, Sweller and Moreno (2015) established 11 design principles that were created to reduce extraneous cognitive load and manage germane and intrinsic loads at an appropriate level for learners using technology (Mayer, Sweller & Moreno, 2015; Wikipedia, 2020). These types of cognitive load, along with design principles and technology, comprise e-learning theory. E-learning theory belongs to the grand theory of Connectivism because it emphasizes how technologies can be used and designed to create new learning opportunities and to promote effective learning.

Previous Studies

Multimedia learning is one specific principle of e-learning theory, and it contends that deeper learning can be promoted using two formats among audio, visual, and text instead of one or three (Mayer, Sweller & Moreno, 2015). Previous studies relevant to e-learning theory have provided evidence that multimedia design principles can foster effective learning (Mayer & Moreno, 2003; Moreno & Mayer, 2007). For example, Mayer (1997) conducted several reviews of multimedia learning and found that multimedia instruction was effective. To be specific, Mayer (1997) reviewed eight studies on whether multimedia instruction was effective and found that students who were given a presentation with both verbal and visual explanations had a 75% higher median score for creative solutions on problem-solving transfer tests than students who experienced only verbal explanation. Ten studies reviewed by Mayer (1997) found that students showed scored more than 50% over the median on creative solution transfer tests when verbal and visual descriptions were concurrently employed.

Personalization is also an essential principle of e-learning theory. This principle suggests that presenting words in a conversational and informal style can help enhance effective learning (Mayer et al., 2015). Several studies have shown that personalization can be effective in learning. For example, Kartal’s (2010) study investigated the effectiveness of the design principle of personalization with 89 college students in an Istanbul university in Turkey by testing their computerized instructional content in a personalized informal style, personalized formal style, and neutral-formal style. The results showed that the amount of learning increased when the language style was formal and conversational.

Another study conducted by Kurt (2011) showed consistent results with Kartal’s (2010) study. Kurt (2011) examined the personalization effect with multimedia material in a formal style with 22 students and conversational style with 23 students. Using an achievement test, a cognitive load scale for both groups, and a questionnaire for the personalized group, Kurt found that students’ cognitive load scores in the personalized group were significantly different from and better than those in the non-personalized group. Besides, students in the personalized group said that the conversational style applied in the multimedia software inspired them to learn and they felt that a real human was talking to them. In addition, students showed a preference for multimedia materials.

Several other studies have also shown other design principles of e-learning theory to be effective. Some researchers studied the modality principle, which claims that the use of visuals accompanied by audio narration instead of on-screen text is more effective for learning (Mayer et al., 2015). For example, Moreno (2006) conducted a meta-analysis on modality effects. The results revealed significant learning benefits due to the modality principle across different media.

As can be seen from the above discussion, applying the principles of e-learning theory with its design principles can promote effective learning. Therefore, e-learning theory can be useful for teachers to design effective courses and for researchers to understand how effective learning with and through technology can happen.

Model of E-learning Theory

The model in Figure 1 demonstrates that concepts of three types of cognitive load and eleven empirical principles compose two constructs: cognitive load and design principles. These two constructs then combine to lead to the proposition of e-learning theory.

Concepts and Constructs

As noted previously, the three cognitive loads are intrinsic, germane, and extraneous based on the amount of mental effort. Intrinsic load is “the mental work imposed by the complexity of the content in your lessons and is primarily determined by your instructional goals” (Clark et al., 2005, p.9). Germane load is “mental work imposed by instructional activities that benefit the instructional goal” (Clark et al., 2005, p.11). Extraneous load is “the mental work that is irrelevant to the learning goal and consequently wastes limited mental resources” (Clark et al, 2005, p.12). Together these form the construct “cognitive load.”

E-learning theory is also composed of principles that can be integrated into instructional design; they that demonstrate “how educational technology can be used and designed to promote effective learning” (Wang, 2012, p.346). The eleven principles of the model that can promote effective learning are:

Multimedia principle: Using two formats of audio, visual, and text instead of using one or three.

Modality principle: Explaining visual content with audio narration instead of on-screen text.

Coherence principle: Avoiding irrelevant videos and audio.

Contiguity principle: Aligning relevant information to corresponding pictures concurrently.

Segmenting principle: Managing complicated content by breaking a lesson into small parts.

Signaling principle: Offering signals for the narration, such as arrows, circles, and highlights.

Learner control principle: Allowing the learner to control their learning pace.

Personalization principle: Presenting words in a conversational and informal style.

Pre-training principle: Providing descriptions or explanations for key concepts in a lesson before the main procedure of that lesson.

Redundancy principle: Presenting visuals with audio or on-screen text but not both.

Expertise effect: Considering that design principles may have a different effect on learners with various amounts of prior knowledge.  (Clark & Mayer, 2016; Mayer, 2003; Mayer & Moreno, 2003; Mayer et al., 2015)

Together, these eleven principles form the construct “design principles.”

Overall, the ideas of cognitive load and design principles can be integrated to reduce extraneous cognitive load and manage germane and intrinsic loads by making it easier for learners’ brains to handle the amount of information and processing that they must do during instructional tasks.

Proposition 

Based on the concepts and constructs, the model ends with the proposition, that if teachers design principled tasks with educational technologies that reduce extraneous cognitive load and manage germane and intrinsic load at appropriate levels for students, they can learn effectively (Mayer, Sweller & Moreno, 2015).

Possible  Ways to Use the Model

Several possible ways exist for using this model in research and practice. For example, researchers can use this model to better understand how design principles can be integrated in instruction to promote effective learning. Researchers can also conduct studies using the e-learning theory model to describe the design principles in learning contexts. In addition, this model can also help researchers address the following topics:

How research-based e-learning methodologies can be used to create an effective e-learning course.

How teachers can minimize extraneous load and manage intrinsic load to help effective learning.

Which design principles could contribute most to effective student learning.

Furthermore, teachers could apply the e-learning theory model in their classrooms to create effective e-learning courses. For example, teachers can help students manage their intrinsic cognitive load by splitting the content so that students can acquire knowledge step by step (Clark et al., 2005). Teachers can also scaffold students with small portions of new content gradually so that students can control their learning in a self-paced e-learning environment (Clark et al., 2005). Further, teachers can use basic digital communication tools with visuals, text, and audio to demonstrate learning content in ways that can help to reduce students’ intrinsic cognitive loads. In addition, teachers can apply effective graphics, audio, and text to minimize redundant content, concentrate on important content, and offer performance assistance to increase external memory. More examples of how teachers can apply e-learning theory in classrooms include:

Reducing extraneous cognitive load by avoiding irrelevant audio or complex visuals to describe complicated text (the coherence principle)

Managing intrinsic cognitive load by segmenting content into small parts and using pretraining to teach concepts and facts separately (the segmenting principle).

Fostering germane cognitive load by adding practice activities and relevant visuals (the modality principle) (Clark & Mayer, 2016).

There is no need to use all eleven principles to enhance students’ learning. Specific design principles can be used in different situations, depending on teachers’ instructional objectives and students’ learning objectives.

E-learning theory is about designing educational technology use to promote effective learning by reducing extraneous cognitive load and managing germane and intrinsic loads at students’ appropriate levels. It can be challenging for teachers to design tasks at an appropriate level for students; the e-learning theory model can help teachers understand how cognitive load can be categorized and combined with design principles to make effective learning with technology happen.

Clark, R.C., & Mayer, R.E. (2016).  E-learning and the science of instruction: Proven guidelines for consumers and designers of multimedia learning  (4th ed.). John Wiley & Sons, Inc.

Clark, R. C., Nguyen, F., & Sweller, J. (2005).  Efficiency in learning: Evidence-based guidelines to manage cognitive load . Pfeiffer.

David, L. (2015, December). E-learning Theory (Mayer, Sweller, Moreno). Learning Theories . https://www.learning-theories.com/e-learning-theory-mayer-sweller-moreno.html.

E-learning theory. (2020, April 11). In  Wikipedia . https://en.wikipedia.org/wiki/E-learning_(theory)

Kartal, G. (2010). Does language matter in multimedia learning? Personalization principle revisited.  Journal of Educational Psychology ,  102 (3), 615.

Kurt, A.A. (2011). Personalization principle in multimedia learning: Conversational versus formal style in written word.  TOJET: The Turkish Online Journal of Educational Technology ,  10 (3), 185-192.

Mayer, R. E. (1997). Multimedia learning: Are we asking the right questions?  Educational Psychologist ,  32 (1), 1–19.

Mayer, R. (2003). Elements of a science of e-learning.  Journal of Educational Computing Research ,  29 (3), 297–313.

Mayer, R.E., Moreno, R., & Sweller, J. (2015). E-learning theory . https://www.learning-theories.com/e-learning-theory-mayer-sweller-moreno.html.

Mayer, R. E., & Moreno, R. (2003). Nine ways to reduce cognitive load in multimedia learning.  Educational Psychologist, 38 (1), 43-52.

Moreno, R. (2006). Does the modality principle hold for different media? A test of the method‐affects‐learning hypothesis.  Journal of Computer Assisted Learning ,  22 (3), 149-158.

Moreno, R., & Mayer, R. (2007). Interactive multimodal learning environments.  Educational Psychology Review ,  19 (3), 309-326.

Sweller, J., Van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later.  Educational Psychology Review , 31( 2 ), 261–292.

Wang, V. C. (2012). Understanding and promoting learning theories.  International Journal of Multidisciplinary Research and Modern Education, 8 (2), 343-347.

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

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1 Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

2.1 Selecting and coding the data (studies)

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

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

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

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

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

2.2 Study group

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

2.3 Publication bias

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

2.4 Selecting the model

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

2.5 Heterogeneity

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

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

2.6 Interpreting the effect sizes

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

3 Findings and results

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4 Conclusions and discussion

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

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

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

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

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

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

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

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

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

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

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

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

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Received : 06 December 2020

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Published : 06 September 2021

Issue Date : January 2022

DOI : https://doi.org/10.1007/s10639-021-10740-8

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Please note you do not have access to teaching notes, study of six online learning theories shows theories should be chosen to match institutional situations and learners' backgrounds.

Human Resource Management International Digest

ISSN : 0967-0734

Article publication date: 23 August 2021

Issue publication date: 8 September 2021

The authors assessed the following six popular online theories: Cognitivism, connectivism, heutagogy, social learning, transformative learning theories and Vygotsky’s zone of proximal development (ZPD). The theories were selected because of their relevance to improving online instruction.

Design/methodology/approach

To compare them, the authors reviewed literature on adult learning theories from the following databases: Academic Search Premier, ERIC and ProQuest. They chose the most relevant articles about each theory published between 2007 and 2017, summarized them and extracted relevant information.

The theories suggest various pointers to help course designers to improve online learning. Based on cognitivism, instructors can use media-based instruction designed especially for the working memory. Similarly, connectivism informs instructors to design instruction integrated with technology. Heutagogy also promotes the integration of technology with online learning and encourages self-directed learning. Meanwhile, social learning theory informs instructors to design group discussions and activities to foster collaboration. The other three theories - cognitivism, connectivism and heutagogy – promote the integration of technology.

Originality/value

The authors said the paper was useful as it provided a theoretical framework for adult instructors and theory designers. The paper was a follow-up to another study by the sane authors of online theories. There are also research implications. While pedagogical frameworks are well-established for online learning, studies on learner motivation would establish a wider understanding of richer design formats, the authors say.

• Social learning
• Behaviorism
• Online instruction
• Cognitivism
• Transformative learning theory

(2021), "Study of six online learning theories shows theories should be chosen to match institutional situations and learners' backgrounds", Human Resource Management International Digest , Vol. 29 No. 6, pp. 5-7. https://doi.org/10.1108/HRMID-06-2021-0144

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