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

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

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

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

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1 Introduction

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

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

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

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

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

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

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

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

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

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

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

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

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

2.1 Selecting and coding the data (studies)

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

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

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

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

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

2.2 Study group

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

2.3 Publication bias

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

2.4 Selecting the model

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

2.5 Heterogeneity

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

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

2.6 Interpreting the effect sizes

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

3 Findings and results

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

4 Conclusions and discussion

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

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

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

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

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

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

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

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

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

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

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

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

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Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction

• Hassan Abuhassna   ORCID: orcid.org/0000-0002-5774-3652 1 ,
• Waleed Mugahed Al-Rahmi 1 ,
• Noraffandy Yahya 1 ,
• Megat Aman Zahiri Megat Zakaria 1 ,
• Azlina Bt. Mohd Kosnin 1 &
• Mohamad Darwish 2  

International Journal of Educational Technology in Higher Education volume  17 , Article number:  38 ( 2020 ) Cite this article

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This research aims to explore and investigate potential factors influencing students’ academic achievements and satisfaction with using online learning platforms. This study was constructed based on Transactional Distance Theory (TDT) and Bloom’s Taxonomy Theory (BTT). This study was conducted on 243 students using online learning platforms in higher education. This research utilized a quantitative research method. The model of this research illustrates eleven factors on using online learning platforms to improve students’ academic achievements and satisfaction. The findings showed that the students’ background, experience, collaborations, interactions, and autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, remembering, understanding, analyzing, and satisfaction was positively aligned with students’ academic achievements. Consequently, the empirical findings present a strong support to the integrative association between TDT and BTT theories in relation to using online learning platforms to improve students’ academic achievements and satisfaction, which could help decision makers in universities and higher education and colleges to plan, evaluate, and implement online learning platforms in their institutions.

Introduction

Higher education organizations over the previous two decades have offered full courses online as an integral part of their curricula, besides encouraging the completion throughout the online courses. Additionally, the number of students who are not participating in any courses online has continued to drop over the past few years. Similarly, it is perfectly possible to state that learning online is obviously an educational platform (Allen, Seaman, Poulin, & Straut, 2016 ). Courses online are trying to connect social networking components, experts’ content, because online resources are growing on daily basis. Such courses depend on active participation of a significant number of learners who participate independently in accordance with their education objectives, skills, and previous background and experience (McAuley, Stewart, Siemens, & Cormier, 2010 ). Nevertheless, learners differ in their previous background and experience, along with their education techniques, which clearly influence their online courses results besides their achievement (Kauffman, 2015 ). Consequently, despite the online learning evolution, learning online possibly will not be appropriate for each learner (Bouhnik & Carmi, 2013 ). Nevertheless, while online learning application among academic world has grown rapidly, not enough is identified regarding learners’ previous background and experience in learning online. Not so long ago, investigation concentrated on particular characteristics of learners’ experiences along with beliefs, for instance collaboration with their own instructor, online course quality, or studying with a certain learning management system (LMS) (Alexander & Golja, 2007 ; (Lester & King, 2009 ). Generally, limited courses or a single institution were investigated (Coates, James, & Baldwin, 2005 ; Lee, Yoon, & Lee, 2009 ). Few studies examined bigger sample sizes between one or more particular institutes (Alexander & Golja, 2007 ). Additionally, there is a shortage of researches that examine learners’ previous background and experience comparing face-to-face along with learning online elements, e.g., (Bliuc, Goodyear, & Ellis, 2007 ). The development of learners’ previous background and experience, skills, are realized to be the major advantages for administrative level for learning online.

Similarly, learners’ satisfaction and academic achievement towards learning online attracted considerable attention from scholars who employed several theoretical models in order to evaluate learners’ satisfaction and academic achievements (Abuhassna, Megat, Yahaya, Azlina, & Al-rahmi, 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi, Othman, & Yusuf, 2015a ; Al-Rahmi, Othman, & Yusuf, 2015b ). This present study highlights the effects of online learning platforms on student’s satisfaction, in relation to their background and prior experiences towards online learning platforms to identify learners that are going to be satisfied toward online course. Furthermore, this research explores the effects of transactional distance theory (TDT); student collaboration, student- instructor dialogue or communication, and student autonomy in relation to their satisfaction. Accordingly, this study investigates students’ academic achievements within online platforms, utilizing Bloom theory to measure students’ achievements through four main components, namely, understanding, remembering, applying, and analyzing. This study could have a significant influence on online course design and development. Additionally, this research may influence not only academic online courses but then other educational organizations according to the fact that several organizations offer training courses and solutions online. Both researchers and Instructors will be able to utilize and elaborate in accordance with the preliminary model, which was developed throughout this research, on the effects of online platforms on student’s satisfaction and academic achievements. Advantages of online learning and along with its applications were mentioned in earlier correlated literature (Abuhassna et al., 2020 ;Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). However, despite the growing usage of online platforms, there is a shortage of employing this technology, which creates an issue in itself (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ). Consequently, the research problem lies in the point that a model needs to be created to locate the significant evidence based on the data of student’s background, experiences and interactions within online learning environments which influence their academic performance and satisfaction. Thus, this developed model must be as a guidance for instructors and decision makers in the online education industry in terms of using online platforms to improve students learning experience through online platforms. Bearing in mind these conditions, our major problem was: how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

Research questions

The major research question that are anticipated to be answered is:

how could we enhance students online learning experience in relation to both their academic achievements and satisfaction?

To be able to answer this question, it is required to examine numerous sub-questions which have been stated as follow:

Q1: What is the relationship between students’ background and students’ satisfaction?

Q2: What is the relationship between students’ experience and students’ satisfaction?

Q3: What is the relationship between students’ collaboration and students’ satisfaction?

Q4: What is the relationship between students’ interaction and students’ satisfaction?

Q5: What is the relationship between students’ autonomy and students’ satisfaction?

Q6: What is the relationship between students’ satisfaction and students’ academic achievements?

Q7: What is the relationship between students’ application and students’ academic achievements?

Q8: What is the relationship between students’ remembering and students’ academic achievements?

Q9: What is the relationship between students’ understanding and students’ academic achievements?

Q10: What is the relationship between students’ analyzing and students’ academic achievements?

Research theory and hypotheses development

When designing web-courses within online learning instructions or mechanisms in general, educators are left with several decisions and considerations to face, which accordingly affect how students experience instruction, how they construct and process knowledge, how students could be satisfied through this experiment, and how web-based learning courses could enhance their academic achievements. In this study, we construct our theoretical framework according to Moore transactional distance theory (TDT) to measure student’s satisfaction, in addition to Bloom theory components to measure students’ academic achievements. Though the origins of TDT can be traced to the work of Dewey, it is Michael Moore who is identified as the innovator of this theory that first appeared in 1972. In his study and development of the theory, he acknowledged three main components of TDT that work as the base for much of the research on DL. Also, Bloom’s Taxonomy was established in 1956 under the direction of educational psychologist to measure students’ academic achievement (Bloom, Engelhart, Furst, Hill, & Krathwohl, 1956 ). TDT theory has been selected in this study since Transactional distance’s term indicates the geographical space between the student and instructor. Based on the learning understanding, which happens through learner’s interaction with his environment. This theory considers the role of each of these elements (Student’s autonomy, Dialogue, and class structure) whereas these three elements could help to investigate student’s satisfaction. Moore’s ( 1990 ) notion of ‘Transactional Distance’ adopt the distance that happens in all relations in education. The distance in the theory is mainly specified the dialogue’s amount which happens between the student and the teacher, and the structure’s amount in the course design. Which serves the main goal of this study as to enhance students online learning experience in relation to their satisfaction. Whereas, Bloom Theory has been selected in this study in addition to TDT to enhance students online learning experience in relation to their student’s achievements. In a conclusion both methods were implemented to develop and hypothesis this study hypothesis. See Fig.  1 .

Research Model and Hypotheses

Hypothesis of the study

H1: There is a significant relationship between students’ background and students’ satisfaction.

H2: There is a significant relationship between students’ experience and students’ satisfaction.

H3: There is a significant relationship between students’ collaboration and students’ satisfaction.

H4: There is a significant relationship between students’ interaction and students’ satisfaction.

H5: There is a significant relationship between students’ autonomy and students’ satisfaction.

H6: There is a significant relationship between students’ satisfaction and students’ academic achievements.

H7: There is a significant relationship between students’ application and students’ academic achievements.

H8: There is a significant relationship between students’ remembering and students’ academic achievements.

H9: There is a significant relationship between students’ understanding and students’ academic achievements.

H10: There is a significant relationship between students’ analyzing and students’ academic achievements.

Hypothesis developments and literature review

This Section of the study will discuss the study hypothesis and relates each hypothesis to its related studies from the literature.

Students background toward online platforms

Students’ background regarding online platforms in this study is referred to as their readiness and willingness to use and adapt to different online platforms, providing them with the needed support and assistance. Students’ background towards online learning is a crucial component throughout this process, as prior research revealed that there are implementation issues, for instance; the deficiency of qualified lecturers, infrastructure and facilities, in addition to students’ readiness, besides students’ resistance to accept online learning platforms in addition to the Learning Management System (LMS) platforms, as educational tools (Azhari & Ming, 2015 ). However, student demand continued to increase, spreading to global audiences due to its exceptional functionality, flexibility and eventual accessibility (Azhari & Ming, 2015 ). There have been persistent apprehensions regarding online learning quality compared with traditional learning settings. In their research, (Paechter & Maier, 2010 ; Panyajamorn, Suthathip, Kohda, Chongphaisal, & Supnithi, 2018 ) have discovered that Austrian learners continue to prefer traditional learning environments due to communication goals, along with the interpersonal relations preservation. Moreover, (Lau & Shaikh, 2012 ) have discovered that Malaysian learners’ internet efficiency and computer skills, along with their personal demographics like gender, background, level of the study, as well as their financial income lead to a significant difference in their readiness towards online learning platforms. Abuhassna and Yahaya ( 2018 ) claimed that the current technologies in education play an essential role in providing a full online learning experience which is close enough to a face-to-face class in spite of the physical separation of the students from their educator, along with other students. Platforms of online learning lend themselves towards a less hierarchical methodology in education, fulfilling the learning desires of individuals which do not approach new information in a linear or a systematic manner. Platforms of online learning additionally are the most suitable ways for autonomous students (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Paechter & Maier, 2010 ; Panyajamorn et al., 2018 ).

Students experience toward online platforms

Students’ experience in the current research indicates that learners must have prior experience in relation to utilizing online learning platform in their education settings. Thus, students experience towards online learning offers several advantages among themselves and their instructors in strengthening students’ learning experiences especially for isolated learners (Jaques & Salmon, 2007 ; Lau & Shaikh, 2012 ; Salmon, 2011 ; Salmon, 2014 ). Regardless of student recognition of the advantages towards supporting their learning throughout utilizing the technology, difficulties may occur through the boundaries about their technical capabilities and prior experiences towards utilizing the software itself from the perspective of its functionality. As demonstrated over learner’s experience and feedback from several online sessions over the years, this may frequently become a frustration source between both learners and their instructors, as this may make typically uncomplicated duties, for instance, watching a video, uploading a document, and other simple tasks to be progressively complicated for them, having no such prior experience. Furthermore, when filling out evaluations, for instance, online group presentations, the relatively limited capability to communicate face-to-face then to rely on a non-verbal signal along with audience’s body language might be a discouraging component. Nonetheless, the significance of being in a position to participate with other colleagues employing online sessions, which are occasionally nonvisual, for instance; teleconference format is a progressively significant skill in the modern workplace, thus affirming the importance of concise, clear, intensive interactions skills (Salmon, 2011 ; Salmon, 2014 ).

Student collaboration among themselves in online platforms

Students’ collaborations in the current study refers to the communication and feedback among themselves in online platforms. To refine and measure transactional distance using a survey tool, (Rabinovich, 2009 ) created a survey instrument to measure transactional distance in a higher education setting. A survey was sent to 235 students enrolled in a synchronous web-based graduate class in business regarding transactional distance and Collaborations (Rabinovich, 2009 ). The synchronous learning environment was described as a place where “live on-campus classes are conveyed simultaneously to both in-class students on campus and remote students on the Web who join via virtual classroom Web collaboration software” (Rabinovich, 2009 ). The virtual classroom software is similar to the characteristics of the two different software described by (Falloon, 2011 ; Mathieson, 2012 ) that it allows for students to interact with the educator and fellow students in real-time (Rabinovich, 2009 ). Moreover, (Kassandrinou, Angelaki, & Mavroidis, 2014 ) reported that the instructor plays a crucial role as interaction and communication helpers, as they are tasked with fostering, reassuring and assisting communication and interaction among students. Face-to-face tutorials have proven to be a vast opportunity for a multitude of students to interchange ideas, argue the content of the course and its related concerns (Vasala & Andreadou, 2010 ).

Students’ interactions with the instructor in online platforms

Purposeful interaction or (dialogue) in the current study describes communication that is learner-learner and learner-instructor which is designed to improve the understanding of the student. According to (Shearer, 2010 ) communication should also be constructive in that it builds upon ideas and work from others, as well as assists others in learning. (Moore, 1972 ) affirmed that learners also must realize that, and value the importance of the learning interactions as a vital part of the learning process. In a manner similar to (Benson & Samarawickrema, 2009 ] study of teacher preparatory students, (Falloon, 2011 ) investigated the use of digital tools in a case study at a teacher education program in New Zealand. (Mathieson, 2012 ) also explored the role dialogue plays in digital learning environments. She created a digital survey that examined students’ perception of audio-visual feedback in courses that utilize screen casting digital tools. (Moore, 2007 ) discusses autonomous learners searching for courses that do not stress structure and dialogue in order explain and enhance their learning progression. (Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2015b ; Al-Rahmi, Othman, & Yusuf, 2015d ; Furnborough, 2012 ) concluded that the feeling of cooperation that learners’ share with their fellow students effect their reaction concerning their collaboration with their peers.

Student autonomy in online platforms

Student autonomy in the current study refers to their independence and motivation towards learning. The learner is the motivation of the way toward learning, along with their expectations and requirements, thinking about everyone as a unique individual and hence investigating their own capacities and possibilities. Thus, extraordinary importance is attributed to autonomy in DL environments, since the option of instructive intercession offered in distance education empowers students towards learning autonomy (Massimo, 2014 ). In this respect, the connection between autonomy of student and explicit parts of the learning procedure are in the center of consideration as mentioned. (Madjar, Nave, & Hen, 2013 ) concluded that a learners’ autonomy-supportive environment provides these learners with adoption of a more aims guided learning, leading to more learning achievements. This is why autonomy is desired in the online settings for both individual development and greater achievement in academic environments. The researchers also indicate in their research that while autonomy supports outcomes in goals and aims guiding, educator practices mainly lead to goals which necessary cannot adapt. Thus, supportive-autonomy learning process needs to be designed with affective elements consideration as well. However, (Stroet, Opdenakker, & Minnaert, 2013 ) efficiently surveyed 71 experimental studies on the impacts of autonomy supportive teaching on motivation of learner and discovered a clear positive correlation. Similar to attribution theory, the relationship between learner control and inspiration involves the possibility of learners adjusting their own inspirations, for example, learners may be competent to change self-determined extrinsic motivation to intrinsic motivation. However, (Jacobs, Renandya, & Power, 2016 ) further indicated that learners will not reach the same level of autonomy without reviewing learner’s autonomy insights, reflecting on their learning experiences, sharing these experiences and reflections with other learners, and realizing the elements influencing all these processes, and the process of learning as well.

Student satisfaction in online platforms

Student satisfaction in the current study refers to the fact that there are many factors that play a role in determining the learner’s satisfaction, such as faculty, institution, individual learner element, interaction/communication elements, the course elements, and learning environment. Discussion of the elements also related to the role of the instructor, with the learner’s attitude, social presence, usefulness, and effectiveness of Online Platforms. (Yu, 2015 ) investigated that student satisfaction was positively associated with interaction, self-efficacy and self-regulation without significant gender variations. (Choy & Quek, 2016 ). examined the relationships between the learners’ perceived teaching, social, and cognitive element. In addition, satisfaction, academic performance, and achievement can be measured using a revised form of the survey instrument. (Kirmizi, 2014 ) studied connection between 6 psychosocial scales: personal relevance, educator assistance, student interaction and collaboration, student autonomy, authentic learning, along with active learning. A moderate level of correlation was found between these mentioned variables. Learner satisfaction predictors were educator support, personal relevance and authentic learning, while authentic learning was the only academic success predictor. Findings of (Bordelon, 2013 ) determined and described a positive correlation between both achievement and satisfaction. He demonstrated that the reasons behind these conclusions could be cultural variations in learner’s satisfaction which point out learning accession Zhu ( 2012 ). Scholars in the field of student satisfaction emphasis on the delivery besides the operational side of the student’s experience in the teaching process (Al-Rahmi, Othman, & Yusuf, 2015e ).

Students’ academic achievements in online platforms

Students achievements in this study refers to Bloom’s main four components of achievements, which are remembering, understanding, applying, and analyzing. Finding in a study conducted by (Whitmer, 2013 ) revealed the relationships between student academic achievement and the LMS usage, thus the findings showed a highly systematic association ( p  < .0000) in relation to every variable. These variables described 12% and 23% of variations within the final course marks, which indicates that learners who employed the LMS more often obtained higher marks than the others. Thus, the correlation techniques examined these variables separately to ascertain their association with the final mark. Moreover, it is not the technology itself; it is the educational methods in relation to which technology has been utilized that create a change in learners’ achievement. Instruments used are significant in identifying the technology impact, moreover, it is the implementation of those instruments under specific activities and for certain purposes which indicates whether or not they are effective. In contrast, a study conducted by (Barkand, 2017 ) revealed that LMS tools were not considered to have an effect on semester final grades when categorized by school year. In his study, semester final grades were a measure of student achievement, which has subjective elements. To account for the subjective elements in semester final grades, the study also included objective post test scores to evaluate student learning. Additionally, in this study, we refer to Bloom’s Taxonomy established in 1956 under the direction of educational psychologist for measuring students’ academic achievement (Bloom et al., 1956 ). Moreover, in this study, we selected fours domains of Blooms Taxonomy in order to achieve this study objectives, which are; application: which refers to using a concept in new context, for instance; applying what has been learned inside the classroom into different circumstances; remembering, which refers to recalling or retrieving prior learned knowledge; understanding, which refers to realizing the meaning, then clarification of problems instructions; analyzing, which refers to separating concepts or material into parts in such a way that its structure can be distinguished, understood among inferences and facts.

Students’ application

Applying involves “carrying out or using a procedure through executing or implementing” (Anderson & Krathwohl, 2001 ). Applying in this study refers to the student’s ability to use online platforms, such as how to log in, how to end session, how to download materials, how to access links and videos. Students can exchange information about a specific topic in online platforms such as Moodle, Google Documents, Wikis and apply knowledge to create and participate in online platforms.

Students’ remembering

Remembering is defined as “retrieving, recognizing, and recalling relevant knowledge from long-term memory” (Anderson & Krathwohl, 2001 ). In this study, remembering is referred to the ability to organize and remember online resources to easily find information on the internet. Moreover, students can easily cooperate with their colleagues and educator, contributing to the educational process and justifying their study procedure. Anderson and Krathwohl ( 2001 ) In their review of Bloom’s taxonomy, Anderson and Krathwohl ( 2001 ) recognized greater learning levels as creating, evaluating, and analyzing, with the lower learning levels as applying, understanding, and remembering.

Students’ understanding

Understanding involves “constructing meaning from oral, written, and graphic messages through interpreting, exemplifying, classifying, summarizing, inferring, comparing, and explaining” (Anderson & Krathwohl, 2001 ). In this study, understanding is referred to as understanding regarding a subject then putting forward new suggestions about online settings, for instance; understanding how e-learning works, or LMS. For example, students use online platforms to review concepts, courses, and prominent resources are being used inside the classroom environment.

Students’ analyzing

Analyzing includes “breaking material into constituent parts, determining how the parts relate to one another and to an overall structure or purpose through differentiating, organizing, and attributing” (Anderson & Krathwohl, 2001 ). Analyzing refers to the student’s ability to connect, discuss, mark-up, then evaluate the information received into one certain workplace or playground. Solomon and Schrum ( 2010 ) claim that educators have started employing online platforms for a range of activities, since they have become more familiar and there are ways for learners to benefit from using them. Generally, the purpose and goal are to publicize the development types, innovation, as well as additional activities that their learners usually do independently. Such instruments have also provided instructors ways to encourage and promote genuine cooperation in their project’s development (Solomon & Schrum, 2010 ).

Research methodology

A quantitative approach was implemented in this study to provide an inclusive insight in relation to students online learning experience and how to enhance both their satisfaction and academic achievements using a questionnaire. Two experts were referred for the evaluation of the questionnaire’s content. Before the collection of the data, permission regarding the current research purpose has been obtained from Universiti Teknologi Malaysia (UTM). In relation to the sampling and population, this research was conducted among undergraduate learners who have been online learning users. Learners, who had manually obtained the questionnaires, have been requested to fill in their details, then fill their own assessments regarding online learning platforms and its effects towards their academic achievements. Thus, for data analysis, the data that were attained from questionnaires were then analyzed using the Statistical Package for the Social Sciences (SPSS). Specifically, Structural Equation Modeling (SEM- Amos), which has been employed as a primary data analysis tool. Moreover, utilizing SEM-Amos process involves two main phases: evaluating construct validity, the convergent validity, along with the discriminant validity of the measurements; then analyzing the structural model. These mentioned two phases followed the recommendations of (Bagozzi, Yi, & Nassen, 1998; Hair, Sarstedt, Ringle, & Mena, 2012a , 2012b ).

Sample characteristics and data collection

A total of 283 questionnaires were distributed manually; of these, only 264, which make up 93.3% of the total number, were returned to the authors. Excluding the 26 incomplete questionnaires, 264 were evaluated employing SPSS. A total of 21 questionnaires have been excluded: 14 were incomplete and 7 having outliners. Thus, the overall number of valid questionnaires was 243 following this exclusion. This exclusion step is being supported by Hair et al. ( 2012a , 2012b ) . Moreover, Venkatesh, Thong, & Xu, 2012 who pointed out that this procedure is essential to be implemented as the existence of outliers could be a reason for inaccurate results. Regarding the respondent’s demographic details: 91 (37.4%) were males, and 152 (62.6%) were females. 149 (61.3%) were in the age range of 18 t0 20 years old, 77 (31.7%) were in the age range of 21 to 24 years old, and 17 (7.0%) were in the age range of 25 to 29 years old. Regarding level of study: 63 (25.9%) were from level 1, 72 (29.6%) were from level 2, 50 (20.6%) were from level 3, and 58 (23.9%) were from level 4.

Measurement instruments

The questionnaire in this study has been developed to fit the study hypothesis. Consequently, it was developed based into both theories that have been utilized in this study. The questionnaire has two main sections, first section aims to measure student satisfaction which is based on the TDT theory variables. Second section of the questionnaire has been developed to measure students’ academic achievement based on Bloom theory. According to Bloom theory there are four variables that measure students’ achievements, which are application, remembering, understanding, analyzing. On that basis the questionnaire has been developed to measure both students’ satisfaction and academic achievements . The construct items were adapted to ensure content validity. This questionnaire consisted of two main sections. First part covered the demographic details of the respondents’ including age, gender, educational level. The second part comprises 51 items which were adapted from previous researches as following; student background, five items, student experience, five items adapted from (Akaslan & Law, 2011 ), student collaborations, and, student interactions items adapted from (Bolliger & Inan, 2012 ), student autonomy, five items adapted from (Barnard et al., 2009 ; Pintrich, Smith, Garcia, & McKeachie, 1991 ), student satisfaction, six items adapted from (The blended learning impact evaluation at UCF is conducted by Research Initiative for Teaching Effectiveness, n.d. ). Moreover, effects of the students’ application, four items, students’ remembering, four items, students’ understanding, four items, students’ analyzing, four items, and students’ academic achievements, four items adapted from (Pekrun, Goetz, & Perry, 2005 ). The questionnaire has been distributed to the students after taking the online course.

Result and analysis

Cronbach’s Alpha reliability coefficient result was 0.917 among all research model factors. Thus, the discriminant validity (DV) assessment was carried out through utilizing three criteria, which are: index between variables, which is expected to be less than 0.80 (Bagozzi, Yi, & Nassen, 1988 ); each construct AVE value must be equal to or higher than 0.50; square of (AVE) between every construct should be higher, in value, than the inter construct correlations (IC) associated with the factor [49]. Furthermore, the crematory factor analysis (CFA) findings along with factor loading (FL) should therefore be 0.70 or above although the Cronbach’s Alpha (CA) results are confirmed to be ≥0.70 [50]. Researchers have also added that composite reliability (CR) is supposed to be ≥0.70.

Model analysis

Current research employed AMOS 23 to analyze the data. Both structural equation modeling (SEM) as well as confirmatory factor analysis (CFA) have been employed as the main analysis tools. Uni-dimensionality, reliability, convergent validity along with discriminant validity have been employed to assess the measurement model. (Bagozzi et al., 1988 ; Byrne, 2010 ; Kline, 2011 ) highlighted that goodness-of-fit guidelines, such as the normed chi-square, chi-square/degree of freedom, normed fit index (NFI), relative fit index (RFI), Tucker-Lewis coefficient (TLI) comparative fit index (CFI), incremental fit index (IFI), the parsimonious goodness of fit index (PGFI), thus, the root mean square error of approximation (RMSEA) besides the root mean-square residual (RMR). All these are tools which could be utilized as the assessment procedures for the model estimation. See Table  1 & Fig.  2 .

Measurement Model

Measurement model

Such type of validity is commonly employed to specify the size difference between a concept and its indicators and other concepts (Hair et al., 2012a , 2012b ). Through analysis in this context, discriminant validity has proven to be positive over all concepts given that values have been over 0.50 (cut-off value) from p  = 0.001 according to Fornell and Larcker ( 1981 ). In line with Hair et al. ( 2012a , 2012b ) . Bagozzi, Yi, & Nassen, (1998), the correlation between items at any two specified constructs must not exceed the square root of the average variance that is shared between them in a single construct. The outcomes values of composite reliability (CR) besides those of Cronbach’s Alpha (CA) remained about 0.70 and over, while the outcomes of the average variance extracted (AVE) remained about 0.50 and higher, indicating that all factor loadings (FL) were significant, thereby fulfilling conventions in the current assessment Bagozzi, Yi, & Nassen, (1998), and Byrne ( 2010 ). Following sections expand on the results of the measurement model. Findings of validity, reliability, average variance extracted (AVE), composite reliability (CR) as well as Cronbach’s Alpha (CA) have all been accepted, which also demonstrated determining the discriminant validity. It is determined that all the values of (CR) vary between 0.812 and 0.917, meaning they are above the cut-off value of 0.70. The (CA) result values also varied between 0.839 and 0.897 exceeding the cut-off value of 0.70. Thus, the (AVE) was similarly higher than 0.50, varying between 0.610 and 0.684. All these findings are positive, thus indicating significant (FLs) and they comply with the conventional assessment guidelines Bagozzi, Yi, & Nassen, (1998), along with Fornell and Larcker ( 1981 ). See Table  2 and Additional file  1 .

Structural model analysis

In the current study, the path modeling analysis has been utilized to examine the impact of students’ academic achievements among higher education institutions through the following factors (students’ background, students’ experience, students’ collaborations, students’ interaction, students’ autonomy, students’ remembering, students’ understanding, students’ analyzing, students’ application, students’ satisfaction), which is based on online learning. The findings are displayed then compared in hypothesis testing discussion. Subsequently, as the second stage, factor analysis (CFA) has being conducted on structural equation modeling (SEM) in order to assess the proposed hypotheses as demonstrated in Fig.  3 .

Findings for the Proposed Model Path analysis

As shown in both Figs.  3 and 4 , all hypotheses have been accepted. Moreover, Table  3 below shows that the fundamental statistics of the model was good, which indicates model validity along with the testing results of the hypotheses through demonstrating the values of unstandardized coefficients besides standard errors of the structural model.

Findings for the Proposed Model T.Values

The first direct five assumptions, students’ background, students’ experience, students’ collaborations, students’ interaction; students’ autonomy with students’ satisfaction, were addressed. In accordance with Fig.  4 and Table 3 , relations between students’ background and students’ satisfaction was (β = .281, t = 5.591, p  < 0.001), demonstrating that the first hypothesis (H1) has suggested a positive and significant relationship. Following hypothesis illustrated the relationship between students’ experience and students’ satisfaction (β = .111, t = 1.951, p  < 0.001), demonstrating that the second hypothesis (H2) proposed a positive and significant relationship. Third hypothesis illustrated the relationship between students’ collaborations and students’ satisfaction (β = .123, t = 2.584, p  < 0.001) demonstrating that the third hypothesis (H3) has suggested a positive and significant relationship. Additionally, the relationship between students’ background and students’ satisfaction was (β = .116, t = 2.212, p < 0.001), indicating that the fourth hypothesis (H4) has suggested a positive and significant relationship. Further to the above-mentioned findings, the relationship between students’ autonomy and students’ satisfaction was (β = .470, t = 7.711, p  < 0.001), demonstrating that the fifth hypothesis (H5) has suggested a positive and significant relationship. Moreover, in the second section, five assumptions were discussed, which are students’ satisfaction, students’ remembering, students’ understanding, students’ analyzing, students’ application along with students’ academic achievements.

As shown in Fig. 4 and Table 3 , the association between students’ satisfaction and students’ academic achievements was (β = .135, t = 3.473, p  < 0.001), demonstrating that the sixth hypothesis (H6) has suggested a positive and significant relationship. Following hypothesis indicated the relationship between students’ application and students’ academic achievements (β = .215, t = 6.361, p  < 0.001), indicating that the seventh hypothesis (H7) has suggested a positive and significant relationship. Thus, the eighth hypothesis indicated the relationship between students’ remembering and students’ academic achievements was (β = .154, t = 4.228, p  < 0.001), demonstrating that the eight hypothesis (H8) has suggested a positive and significant relationship. Additionally, the correlation between students’ understanding and students’ academic achievements was (β = .252, t = 6.513, p < 0.001), demonstrating that the ninth hypothesis (H9) has suggested a positive and significant relationship. Finally, the relationship between students’ analyzing and students’ academic achievements was (β = .179, t = 6.215, p < 0.001), demonstrating that the tenth hypothesis (H10) has suggested a positive and significant relationship. Accordingly, this current model demonstrated student’s compatibility to use online learning platforms to improve students’ academic achievements and satisfaction. This is in accordance with earlier investigations (Abuhassna & Yahaya, 2018 ; Al-Rahmi et al., 2018 ; Al-rahmi, Othman, & Yusuf, 2015c ; Barkand, 2017 ; Madjar et al., 2013 ; Salmon, 2014 ).

Discussion and implications

Developing a new hybrid technology acceptance model through combining TDT and BTT has been the major objective of the current research, which aimed to investigate the guiding factors towards utilizing online learning platforms to improve students’ academic achievements and satisfaction in higher education institutions. The current research is intensifying a step forward by implementing TDT along with a BTT model. Using the proposed model, the current research examined how students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy positively affected students’ satisfaction. Moreover, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. The current research found that students’ background, students’ experience, students’ collaborations, students’ interactions, and students’ autonomy were influenced by students’ satisfaction. Also, effects of the students’ application, students’ remembering, students’ understanding, students’ analyzing, and students’ satisfaction positively affected students’ academic achievements. This conclusion is consistent with earlier correlated literature. Thus, this reveals that learners first make sure whether using platforms of online learning were able to meet their study requirements, or that using platforms of online learning are relevant to their study process before considering employing such technology in their study. Learners have been noted to perceive that platforms of online learning is more useful only once they discover that such a technology is actually better than the traditional learning which does not include online learning platforms (Choy & Quek, 2016 ; Illinois Online Network, 2003 ). Using the proposed model, the current research examined how to improve students’ academic achievements and satisfaction. Thus, the following section will be a comparison between this study results and previous research, as follows.

The first hypotheses of this study demonstrated a positive and significant association between students’ prior background towards online platforms with their satisfaction. As clearly investigated in Osika and Sharp ( 2002 ) study, numerous learners deprived of these main skills enroll in the courses, struggle, and subsequently drop out. In addition, Bocchi, Eastman, and Swift ( 2004 ) investigation claimed that prior knowledge of students’ concerns, demands along with their anticipations is crucial in constructing an efficient instruction. Thus, to clarify, students must have prior knowledge and background before letting them into the online platforms. On the other hand, there are constant concerns about the online learning platforms quality in comparison to a face-to-face learning environment, as students do not have the essential skills required toward using online learning platforms (Illinois Online Network, 2003 ). Moreover, a study by Alalwan et al. ( 2019 ) discovered that Austrian learners still would rather choose face-to-face learning for communication purposes, and the preservation of interpersonal relations. This is due to the fact that learners do not as yet have the background knowledge and skills needed towards using online learning platforms. Additional research by Orton-Johnson ( 2009 ) among UK learners claimed that learners have not accepted online materials, and continue to prefer traditional context materials as the medium for their learning, which also indicates the importance of prior knowledge and background towards online platforms before going through such a technology.

The second hypotheses of this study proposed a positive and significant association between students’ experience along with students’ satisfaction, which revealed that putting the students in such an experience would provide and support them with the ability to overcome all difficulties that arise through the limits around the technical ability of the online platforms. This is in line with some earlier researches regarding the reasons that lead to people’s technology acceptance behavior. One reason is the notion of “conformity,” which means the degree to which an individual take into consideration that an innovation is consistent with their existing demands, experiences, values and practices (Chau & Hu, 2002 ; Moore & Benbasat, 1991 ; Rogers, 2003 ; Taylor & Todd, 1995 ). Moreover, (Anderson & Reed, 1998 ; Galvin, 2003 ; Lewis, 2004 ) claimed that most students who had prior experience with online education tended to exhibit positive attitudes toward online education, and it affects their attitudes toward online learning platforms.

The third hypotheses of this study demonstrated a positive and significant association among student collaboration with themselves in online platforms, which indicates the key role of collaboration between students in order to make the experiment more realistic and increase their ability to feel more involved and active. This is agreement with Al-rahmi, Othman, and Yusuf ( 2015f ) who claimed that type, quality, and amount of feedback that each student received was correlated to a student’s sense of success or course satisfaction. Moreover, Rabinovich ( 2009 ) found that all types of dialogue were important to transactional distance, which make it easier for the student to adapt to online learning platform. Also, online learning platforms enable learners to share then exchange information among their colleagues Abuhassna et al., 2020 ; Abuhassna & Yahaya, 2018 ).

Students’ interaction with the instructor in online platforms

The fourth hypothesis of this study proposed a positive and significant correlation between students’ collaborations and students’ satisfaction, which indicates the significance of the communication between students and their instructor throughout the online platforms experiment. These results agree with (Mathieson, 2012 ) results, which stated that the ability of communication between students and their instructor lowered the sense of separation between learner and educator. Moreover, in line with (Kassandrinou et al., 2014 ), communication guides learners to undergo constructive emotions, for example relief, satisfaction and excitement, which assist them to achieve their educational goals. In addition, (Furnborough, 2012 ) draws conclusion that learners’ feeling of cooperating with their fellow students effects their reaction concerning their collaboration with their peers. Moreover, Kassandrinou et al., 2014 focused on the instructor as crucial part as interaction and communication helpers, as they are thought to constantly foster, reassure and assist communication and interaction amongst students.

Student’s autonomy in online platforms

The fifth hypotheses of this study proposed a positive and significant relationship between student’s autonomy and online learning platforms, which indicates that students need a sense of dependence towards online platforms, which agrees with Madjar et al. ( 2013 ) who concluded that a learners’ autonomy-supportive environment provides these learners with adoption of more aims, leading to more learning achievements. Moreover, Stroet et al. ( 2013 ) found a clear positive correlation on the impacts of autonomy supportive teaching on motivation of learner. O’Donnell, Chang, and Miller ( 2013 ) also argues that autonomy is the ability of the learners to govern themselves, especially in the process of making decisions and setting their own course and taking responsibility for their own actions.

Student’s satisfaction in online platforms

The sixth hypotheses of this study proposed a positive and significant correlation between student’s satisfaction with online learning platforms, which indicates a level of acceptance by the students to adapt into online learning platforms. This is in agreement with Zhu ( 2012 ) who reported that student’s satisfaction in online platforms is a statement of confidence with the system. Moreover, Kirmizi ( 2014 ) study revealed that the predictors of the learners’ satisfaction were educator’s support, personal relevance and authentic learning, whereas the authentic learning is only the predictor of academic success. Furthermore, the findings of Bordelon ( 2013 ) stated and determined a positive correlation between both satisfaction and achievement. In addition, the results of Mahle ( 2011 ) clarified that student satisfaction occurs when it is realized that the accomplishment has met the learners’ expectations, which is then considered a short-term attitude toward the learning procedure.

Hypotheses seven, eight, nine and ten of this study proposed a positive and significant relationship between student’s academic achievements with online learning platforms, which indicates the key main role of online platform with students’ academic achievements. This agrees with Whitmer ( 2013 ) findings, which revealed that the associations between student usage of the LMS and academic achievement exposed a highly systematic relationship. In contrast, Barkand ( 2017 ) found that there is no significant difference in students’ academic achievements in utilizing online platforms regarding students’ academic achievements, which is due to the fact that academic achievement towards online learning platforms requires a certain set of skills and knowledge as mentioned in the above sections in order to make such technology a success.

The seventh hypotheses of this study proposed a positive and significant correlation between students’ application and students’ academic achievements, which indicates the major key of applying in the learning process as an effected element. This is in line with the Computer Science Teachers’ Association (CSTA) taskforce in the U. S (Computer Science Teachers’ Association (CSTA), 2011 ), where they mentioned that applying elements of computer skills is essential in all state curricula, directing to their value for improving pupils’ higher order thinking in addition to general problem-solving abilities. Moreover, Gouws, Bradshaw, and Wentworth ( 2013 ) created a theoretical framework which drawn education computational thoughts compared to cognitive levels established from Bloom’s Taxonomy of Learning Purposes. Four thinking skill levels have been utilized to assess the ‘cognitive demands’ initiated by computational concepts for instance abstraction, modelling, developing algorithms, generating automated processes. Through the iPad app, LightBot. thinking skills remained recognizing (which means recognize and recall expertise correlating to the problem); Understanding (interpret, compare besides explain the problem); whereas, applying (make use of computer skills to create a solution) then Assimilating (critically decompose and analyses the problem).

The eighth hypotheses of this study proposed a positive and significant correlation between students’ remembering and students’ academic achievements, which indicates the importance of remembering as a process of retrieving information relating to what needed to be done and/or outcome attributes) over the procedure of learning according to Bloom’s Taxonomy of Educational Objectives. Additionally, Falloon ( 2016 ) claimed that responding to data indicated the use of general thinking skills to clarify and understand steps and stages needed to complete a task (average 29%); recalling or remembering information about a task or available tools (average 13%); and discussing and understanding success criteria (average 3%).

The ninth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which indicates its significance with the academic achievements as a process of criticizing the task or the problem faced by the students into phases or activities to help understanding of how to resolve the problem. The current results agree with Falloon ( 2016 ) who demonstrated the necessity to build understanding over the thinking processes employed by students once they are engaged in their work. In addition, Falloon ( 2016 ) suggested that the purpose and nature of questioning was broader than this, with questioning of self and others being an important strategy in solution development. In many respects, the questioning for those students was not much a perspective, although more a practice, to the degree that assisted them to understand their tasks, analyze intended or developed explanations and to evaluate their outcomes.

The tenth hypotheses of this study proposed a positive and significant correlation between students’ understanding and students’ academic achievements, which reveals the importance of analysis as a process of employing general thinking besides computational knowledge in order to realize the challenges through using online platforms, in addition to predictive thinking to categorize, explore and fix any possible errors throughout the whole process. Falloon ( 2016 ) claimed that analyzing was often a collaborative procedure between pairs receiving and giving counseling from others to assist in solving complications. On the other hand, online learning platforms are highly dependent on connecting and sharing as a basic strategy that needs to be employed over all stages of online learning settings, whether between students and students, or between students and their instructor. Moreover, Falloon ( 2016 ) findings showed that Analyzing (average 17%) was present in various phases of these online students’ work, which is based on what phase they were at together with their tasks, despite the fact that most analysis was associated with students depending on themselves during online process.

Conclusion and future work

In this investigation, both transactional distance theory (TDT) and Bloom’s Taxonomy theory (BTT) have been validated in the educational context, providing further understanding towards the students’ prospective perceptions on using online learning platforms to improve students’ academic achievement and satisfaction. The contribution that the current research might have to the field of online learning platforms have been discussed and explained. Additional insights towards students’ satisfactions and students’ academic achievements have also been presented. The current research emphasizes that the incorporation of both TDT and BTT can positively influence the research outcome. The current research has determined that numerous stakeholders, for instance developers, system designers, along with institutional users of online learning platforms reasonably consider student demands and needs, then ensure that the such a system is effectively meeting their requirements and needs. Adoption among users of online learning platforms could be broadly clarified by the eleven factor features which is based on this research model. Thus, the current research suggests more investigation be carried out to examine relationships among the complexity of online learning platforms combined with technology acceptance model (TAM).

Recommendations for stakeholders of online platforms

Based on the study findings, the first recommendation would be for administrators of higher institution. In order to implement online learning, there must be more interest given to the course structure design, whereas it should be based on theories and prior literature. Moreover, instructor and course developer need to be trained and skilled to achieve online learning platforms goals. Workshops and training sessions must be given for both instructors and students to make them more familiar in order to take the most advantages of the learning management system like Moodle and LMS. The software itself is not enough for creating an online learning environment that is suitable for students and instructors. If instructors were not trained and unaware of utilizing the software (e.g. Moodle) in the class, then the quality of education imparted to students will be jeopardized. Training and assessing the class instructor and making modifications to the software could result in a good environment for the instructor and a quality education for the student. Both students’ satisfaction and academic achievements depends on their prior knowledge and experience in relation to online learning. This current research intended to investigate student satisfaction and academic achievements in relation to online learning platforms in on of the higher education in Malaysia. Future research could integrate more in relation to blended learning settings.

Availability of data and materials

All the hardcopy questionnaires, data and statistical analysis are available.

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Abuhassna, H., Al-Rahmi, W.M., Yahya, N. et al. Development of a new model on utilizing online learning platforms to improve students’ academic achievements and satisfaction. Int J Educ Technol High Educ 17 , 38 (2020). https://doi.org/10.1186/s41239-020-00216-z

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Impact of Online Classes on Students Essay

• Introduction
• Thesis Statement

Background study

• Impacts of online education

Introduction to Online Education

Online learning is one of the new innovative study methods that have been introduced in the pedagogy field. In the last few years, there has been a great shift in the training methods. Students can now learn remotely using the internet and computers.

Online learning comes in many forms and has been developing with the introduction of new technologies. Most universities, high schools, and other institutions in the world have all instituted this form of learning, and the student population in the online class is increasing fast. There has been a lot of research on the impacts of online education as compared to ordinary classroom education.

If the goal is to draw a conclusion of online education, considerable differences between the online learning environment and classroom environment should be acknowledged. In the former, teachers and students don’t meet physically as opposed to the latter, where they interact face to face. In this essay, the challenges and impact of online classes on students, teachers, and institutions involved were examined.

Thesis Statement about Online Classes

Thus, the thesis statement about online classes will be as follows:

Online learning has a positive impact on the learners, teachers, and the institution offering these courses.

Online learning or E learning is a term used to describe various learning environments that are conducted and supported by the use of computers and the internet. There are a number of definitions and terminologies that are used to describe online learning.

These include E learning, distance learning, and computer learning, among others (Anon, 2001). Distant learning is one of the terminologies used in E learning and encompasses all learning methods that are used to train students that are geographically away from the training school. Online learning, on the other hand, is used to describe all the learning methods that are supported by the Internet (Moore et al., 2011).

Another terminology that is used is E learning which most authors have described as a learning method that is supported by the use of computers, web-enabled communication, and the use of new technological tools that enhance communication (Spector, 2008). Other terminologies that are used to describe this form of online learning are virtual learning, collaborative learning, web-based learning, and computer-supported collaborative learning (Conrad, 2006).

Impacts of Online Classes on Students

Various studies and articles document the merits, demerits, and challenges of online studies. These studies show that online study is far beneficial to the students, teachers, and the institution in general and that the current challenges can be overcome through technological advancement and increasing efficiency of the learning process.

One of the key advantages of online learning is the ability of students to study in their own comfort. For a long time, students had to leave their comfort areas and attend lectures. This change in environment causes a lack of concentration in students. In contrast, E-learning enables the students to choose the best environment for study, and this promotes their ability to understand. As a result, students enjoy the learning process as compared to conventional classroom learning.

Another benefit is time and cost savings. Online students are able to study at home, and this saves them travel and accommodation costs. This is in contrast with the classroom environment, where learners have to pay for transport and accommodation costs as well as any other costs associated with the learning process.

Online study has been found to reduce the workload on the tutors. Most of the online notes and books are availed to the students, and this reduces the teacher’s workload. Due to the availability of teaching materials online, tutors are not required to search for materials. Teachers usually prepare lessons, and this reduces the task of training students over and over again.

Accessibility to learning materials is another benefit of online learning. Students participating in online study have unlimited access to learning materials, which gives them the ability to study effectively and efficiently. On the other hand, students in the classroom environment have to take notes as the lecture progress, and these notes may not be accurate as compared to the materials uploaded on the websites.

Unlimited resources are another advantage of online study. Traditionally, learning institutions were limited in the number of students that could study in the classroom environment. The limitations of facilities such as lecture theaters and teachers limited student enrollment in schools (Burgess & Russell, 2003).

However, with the advent of online studies, physical limitations imposed by classrooms, tutors, and other resources have been eliminated. A vast number of students can now study in the same institution and be able to access the learning materials online. The use of online media for training enables a vast number of students to access materials online, and this promotes the learning process.

Promoting online study has been found by most researchers to open the students to vast resources that are found on the internet. Most of the students in the classroom environment rely on the tutors’ notes and explanations for them to understand a given concept.

However, students using the web to study most of the time are likely to be exposed to the vast online educational resources that are available. This results in the students gaining a better understanding of the concept as opposed to those in the classroom environment (Berge & Giles, 2008).

An online study environment allows tutors to update their notes and other materials much faster as compared to the classroom environment. This ensures that the students receive up-to-date information on a given study area.

One of the main benefits of E-learning to institutions is the ability to provide training to a large number of students located in any corner of the world. These students are charged training fees, and this increases the money available to the institution. This extra income can be used to develop new educational facilities, and these will promote education further (Gilli et al., 2002).

Despite the many advantages that online study has in transforming the learning process, there are some challenges imposed by the method. One of the challenges is the technological limitations of the current computers, which affect the quality of the learning materials and the learning process in general.

Low download speed and slow internet connectivity affect the availability of learning materials. This problem is, however, been reduced through the application of new software and hardware elements that have high access speeds. This makes it easier to download learning materials and applications. As computing power increases, better and faster computers are being unveiled, and these will enable better access to online study facilities.

Another disadvantage of online learning as compared to the classroom environment is the lack of feedback from the students. In the classroom environment, students listen to the lecture and ask the tutors questions and clarifications any issues they didn’t understand. In the online environment, the response by the teacher may not be immediate, and students who don’t understand a given concept may find it hard to liaise with the teachers.

The problem is, however, been circumvented by the use of simple explanation methods, slideshows, and encouraging discussion forums between the teachers and students. In the discussion forums, students who don’t understand a concept can leave a comment or question, which will be answered by the tutor later.

Like any other form of learning, online studies have a number of benefits and challenges. It is, therefore, not logical to discredit online learning due to the negative impacts of this training method. Furthermore, the benefits of e-learning far outweigh the challenges.

Conclusion about Online Education

In culmination, a comparative study between classroom study and online study was carried out. The study was done by examining the findings recorded in books and journals on the applicability of online learning to students. The study revealed that online learning has many benefits as compared to conventional learning in the classroom environment.

Though online learning has several challenges, such as a lack of feedback from students and a lack of the proper technology to effectively conduct online learning, these limitations can be overcome by upgrading the E-Leaning systems and the use of online discussion forums and new web-based software.

In conclusion, online learning is beneficial to the students, tutors, and the institution offering these courses. I would therefore recommend that online learning be implemented in all learning institutions, and research on how to improve this learning process should be carried out.

Anon, C. (2001). E-learning is taking off in Europe. Industrial and Commercial Training , 33 (7), 280-282.

Berge, Z., & Giles, L. (2008). Implementing and sustaining e-learning in the workplace. International Journal of Web-Based Learning and Teaching Technologies , 3(3), 44-53.

Burgess, J. & Russell, J. (2003).The effectiveness of distance learning initiatives in organizations. Journal of Vocational Behaviour , 63 (2),289-303.

Conrad, D. (2006). E-Learning and social change, Perspectives on higher education in the digital age . New York: Nova Science Publishers.

Gilli, R., Pulcini, M., Tonchia, S. & Zavagno, M. (2002), E-learning: A strategic Instrument. International Journal of Business Performance Management , 4 (1), 2-4.

Moore, J. L., Camille, D. & Galyen, K. (2011). E-Learning, online learning and distance learning environments: Are they the same? Internet and Higher Education, 14(1), 129-135.

Spector, J., Merrill, M., Merrienboer, J. & Driscoll, M. P. (2008). Handbook of research on educational communications and technology (3rd ed.), New York: Lawrence Erlbaum Associates.

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Bibliography

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• The Concept of Medical Terminology
• Healthcare Terminologies and Classification Systems
• Medical terminology
• Medical terminology errors
• Research Process and Terminology: Criminal Justice
• Medical Terminology as a Communication Barrier
• The Impact of Standardized Nursing Terminology
• Students With Children and Teachers’ High Expectations
• Medical Terminology Abbreviations
• Nursing Terminologies: NANDA International
• Strategies for Motivating Students
• Importance of Sexual Education in School
• New School Program in Seattle
• General Education Courses
• E-learning as an Integral Part of Education System

• Open access
• Published: 16 September 2021

Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study

• Meixun Zheng 1 ,
• Daniel Bender 1 &
• Cindy Lyon 1  

BMC Medical Education volume  21 , Article number:  495 ( 2021 ) Cite this article

204k Accesses

72 Citations

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Metrics details

The COVID-19 pandemic forced dental schools to close their campuses and move didactic instruction online. The abrupt transition to online learning, however, has raised several issues that have not been resolved. While several studies have investigated dental students’ attitude towards online learning during the pandemic, mixed results have been reported. Additionally, little research has been conducted to identify and understand factors, especially pedagogical factors, that impacted students’ acceptance of online learning during campus closure. Furthermore, how online learning during the pandemic impacted students’ learning performance has not been empirically investigated. In March 2020, the dental school studied here moved didactic instruction online in response to government issued stay-at-home orders. This first-of-its-kind comparative study examined students’ perceived effectiveness of online courses during summer quarter 2020, explored pedagogical factors impacting their acceptance of online courses, and empirically evaluated the impact of online learning on students’ course performance, during the pandemic.

The study employed a quasi-experimental design. Participants were 482 pre-doctoral students in a U.S dental school. Students’ perceived effectiveness of online courses during the pandemic was assessed with a survey. Students’ course grades for online courses during summer quarter 2020 were compared with that of a control group who received face-to-face instruction for the same courses before the pandemic in summer quarter 2019.

Survey results revealed that most online courses were well accepted by the students, and 80 % of them wanted to continue with some online instruction post pandemic. Regression analyses revealed that students’ perceived engagement with faculty and classmates predicted their perceived effectiveness of the online course. More notably, Chi Square tests demonstrated that in 16 out of the 17 courses compared, the online cohort during summer quarter 2020 was equally or more likely to get an A course grade than the analogous face-to-face cohort during summer quarter 2019.

Conclusions

This is the first empirical study in dental education to demonstrate that online courses during the pandemic could achieve equivalent or better student course performance than the same pre-pandemic in-person courses. The findings fill in gaps in literature and may inform online learning design moving forward.

Peer Review reports

Introduction

Research across disciplines has demonstrated that well-designed online learning can lead to students’ enhanced motivation, satisfaction, and learning [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. A report by the U.S. Department of Education [ 8 ], based on examinations of comparative studies of online and face-to-face versions of the same course from 1996 to 2008, concluded that online learning could produce learning outcomes equivalent to or better than face-to-face learning. The more recent systematic review by Pei and Wu [ 9 ] provided additional evidence that online learning is at least as effective as face-to-face learning for undergraduate medical students.

To take advantage of the opportunities presented by online learning, thought leaders in dental education in the U.S. have advocated for the adoption of online learning in the nation’s dental schools [ 10 , 11 , 12 ]. However, digital innovation has been a slow process in academic dentistry [ 13 , 14 , 15 ]. In March 2020, the COVID-19 pandemic brought unprecedented disruption to dental education by necessitating the need for online learning. In accordance with stay-at-home orders to prevent the spread of the virus, dental schools around the world closed their campuses and moved didactic instruction online.

The abrupt transition to online learning, however, has raised several concerns and question. First, while several studies have examined dental students’ online learning satisfaction during the pandemic, mixed results have been reported. Some studies have reported students’ positive attitude towards online learning [ 15 , 16 , 17 , 18 , 19 , 20 ]. Sadid-Zadeh et al. [ 18 ] found that 99 % of the surveyed dental students at University of Buffalo, in the U.S., were satisfied with live web-based lectures during the pandemic. Schlenz et al. [ 15 ] reported that students in a German dental school had a favorable attitude towards online learning and wanted to continue with online instruction in their future curriculum. Other studies, however, have reported students’ negative online learning experience during the pandemic [ 21 , 22 , 23 , 24 , 25 , 26 ]. For instance, dental students at Harvard University felt that learning during the pandemic had worsened and engagement had decreased [ 23 , 24 ]. In a study with medical and dental students in Pakistan, Abbasi et al. [ 21 ] found that 77 % of the students had negative perceptions about online learning and 84 % reported reduced student-instructor interactions.

In addition to these mixed results, little attention has been given to factors affecting students’ acceptance of online learning during the pandemic. With the likelihood that online learning will persist post pandemic [ 27 ], research in this area is warranted to inform online course design moving forward. In particular, prior research has demonstrated that one of the most important factors influencing students’ performance in any learning environment is a sense of belonging, the feeling of being connected with and supported by the instructor and classmates [ 28 , 29 , 30 , 31 ]. Unfortunately, this aspect of the classroom experience has suffered during school closure. While educational events can be held using a video conferencing system, virtual peer interaction on such platforms has been perceived by medical trainees to be not as easy and personal as physical interaction [ 32 ]. The pandemic highlights the need to examine instructional strategies most suited to the current situation to support students’ engagement with faculty and classmates.

Furthermore, there is considerable concern from the academic community about the quality of online learning. Pre-pandemic, some faculty and students were already skeptical about the value of online learning [ 33 ]. The longer the pandemic lasts, the more they may question the value of online education, asking: Can online learning during the pandemic produce learning outcomes that are similar to face-to-face learning before the pandemic? Despite the documented benefits of online learning prior to the pandemic, the actual impact of online learning during the pandemic on students’ academic performance is still unknown due to reasons outlined below.

On one hand, several factors beyond the technology used could influence the effectiveness of online learning, one of which is the teaching context [ 34 ]. The sudden transition to online learning has posed many challenges to faculty and students. Faculty may not have had adequate time to carefully design online courses to take full advantage of the possibilities of the online format. Some faculty may not have had prior online teaching experience and experienced a deeper learning curve when it came to adopting online teaching methods [ 35 ]. Students may have been at the risk of increased anxiety due to concerns about contracting the virus, on time graduation, finances, and employment [ 36 , 37 ], which may have negatively impacted learning performance [ 38 ]. Therefore, whether online learning during the pandemic could produce learning outcomes similar to those of online learning implemented during more normal times remains to be determined.

Most existing studies on online learning in dental education during the pandemic have only reported students’ satisfaction. The actual impact of the online format on academic performance has not been empirically investigated. The few studies that have examined students’ learning outcomes have only used students’ self-reported data from surveys and focus groups. According to Kaczmarek et al. [ 24 ], 50 % of the participating dental faculty at Harvard University perceived student learning to have worsened during the pandemic and 70 % of the students felt the same. Abbasi et al. [ 21 ] reported that 86 % of medical and dental students in a Pakistan college felt that they learned less online. While student opinions are important, research has demonstrated a poor correlation between students’ perceived learning and actual learning gains [ 39 ]. As we continue to navigate the “new normal” in teaching, students’ learning performance needs to be empirically evaluated to help institutions gauge the impact of this grand online learning experiment.

Research purposes

In March 2020, the University of the Pacific Arthur A. Dugoni School of Dentistry, in the U.S., moved didactic instruction online to ensure the continuity of education during building closure. This study examined students’ acceptance of online learning during the pandemic and its impacting factors, focusing on instructional practices pertaining to students’ engagement/interaction with faculty and classmates. Another purpose of this study was to empirically evaluate the impact of online learning during the pandemic on students’ actual course performance by comparing it with that of a pre-pandemic cohort. To understand the broader impact of the institutional-wide online learning effort, we examined all online courses offered in summer quarter 2020 (July to September) that had a didactic component.

This is the first empirical study in dental education to evaluate students’ learning performance during the pandemic. The study aimed to answer the following three questions.

How well was online learning accepted by students, during the summer quarter 2020 pandemic interruption?

How did instructional strategies, centered around students’ engagement with faculty and classmates, impact their acceptance of online learning?

How did online learning during summer quarter 2020 impact students’ course performance as compared with a previous analogous cohort who received face-to-face instruction in summer quarter 2019?

This study employed a quasi-experimental design. The study was approved by the university’s institutional review board (#2020-68).

Study context and participants

The study was conducted at the Arthur A. Dugoni School of Dentistry, University of the Pacific. The program runs on a quarter system. It offers a 3-year accelerated Doctor of Dental Surgery (DDS) program and a 2-year International Dental Studies (IDS) program for international dentists who have obtained a doctoral degree in dentistry from a country outside the U.S. and want to practice in the U.S. Students advance throughout the program in cohorts. IDS students take some courses together with their DDS peers. All three DDS classes (D1/DDS 2023, D2/DDS 2022, and D3/DDS 2021) and both IDS classes (I1/IDS 2022 and I2/IDS 2021) were invited to participate in the study. The number of students in each class was: D1 = 145, D2 = 143, D3 = 143, I1 = 26, and I2 = 25. This resulted in a total of 482 student participants.

During campus closure, faculty delivered remote instruction in various ways, including live online classes via Zoom @  [ 40 ], self-paced online modules on the school’s learning management system Canvas @  [ 41 ], or a combination of live and self-paced delivery. For self-paced modules, students studied assigned readings and/or viewings such as videos and pre-recorded slide presentations. Some faculty also developed self-paced online lessons with SoftChalk @  [ 42 ], a cloud-based platform that supports the inclusion of gamified learning by insertion of various mini learning activities. The SoftChalk lessons were integrated with Canvas @  [ 41 ] and faculty could monitor students’ progress. After students completed the pre-assigned online materials, some faculty held virtual office hours or live online discussion sessions for students to ask questions and discuss key concepts.

Data collection and analysis

Student survey.

Students’ perceived effectiveness of summer quarter 2020 online courses was evaluated by the school’s Office of Academic Affairs in lieu of the regular course evaluation process. A total of 19 courses for DDS students and 10 courses for IDS students were evaluated. An 8-question survey developed by the researchers (Additional file 1 ) was administered online in the last week of summer quarter 2020. Course directors invited student to take the survey during live online classes. The survey introduction stated that taking the survey was voluntary and that their anonymous responses would be reported in aggregated form for research purposes. Students were invited to continue with the survey if they chose to participate; otherwise, they could exit the survey. The number of students in each class who took the survey was as follows: D1 ( n  = 142; 98 %), D2 ( n  = 133; 93 %), D3 ( n  = 61; 43 %), I1 ( n  = 23; 88 %), and I2 ( n  = 20; 80 %). This resulted in a total of 379 (79 %) respondents across all classes.

The survey questions were on a 4-point scale, ranging from Strongly Disagree (1 point), Disagree (2 points), Agree (3 points), and Strongly Agree (4 points). Students were asked to rate each online course by responding to four statements: “ I could fully engage with the instructor and classmates in this course”; “The online format of this course supported my learning”; “Overall this online course is effective.”, and “ I would have preferred face-to-face instruction for this course ”. For the first three survey questions, a higher mean score indicated a more positive attitude toward the online course. For the fourth question “ I would have preferred face-to-face instruction for this course ”, a higher mean score indicated that more students would have preferred face-to-face instruction for the course. Two additional survey questions asked students to select their preferred online delivery method for fully online courses during the pandemic from three given choices (synchronous online/live, asynchronous online/self-paced, and a combination of both), and to report whether they wanted to continue with some online instruction post pandemic. Finally, two open-ended questions at the end of the survey allowed students to comment on the aspects of online format that they found to be helpful and to provide suggestion for improvement. For the purpose of this study, we focused on the quantitative data from the Likert-scale questions.

Descriptive data such as the mean scores were reported for each course. Regression analyses were conducted to examine the relationship between instructional strategies focusing on students’ engagement with faculty and classmates, and their overall perceived effectiveness of the online course. The independent variable was student responses to the question “ I could fully engage with the instructor and classmates in this course ”, and the dependent variable was their answer to the question “ Overall, this online course is effective .”

Student course grades

Using Chi-square tests, student course grade distributions (A, B, C, D, and F) for summer quarter 2020 online courses were compared with that of a previous cohort who received face-to-face instruction for the same course in summer quarter 2019. Note that as a result of the school’s pre-doctoral curriculum redesign implemented in July 2019, not all courses offered in summer quarter 2020 were offered in the previous year in summer quarter 2019. In other words, some of the courses offered in summer quarter 2020 were new courses offered for the first time. Because these new courses did not have a previous face-to-face version to compare to, they were excluded from data analysis. For some other courses, while course content remained the same between 2019 and 2020, the sequence of course topics within the course had changed. These courses were also excluded from data analysis.

After excluding the aforementioned courses, it resulted in a total of 17 “comparable” courses that were included in data analysis (see the subsequent section). For these courses, the instructor, course content, and course goals were the same in both 2019 and 2020. The assessment methods and grading policies also remained the same through both years. For exams and quizzes, multiple choice questions were the dominating format for both years. While some exam questions in 2020 were different from 2019, faculty reported that the overall exam difficulty level was similar. The main difference in assessment was testing conditions. The 2019 cohort took computer-based exams in the physical classroom with faculty proctoring, and the 2020 cohort took exams at home with remote proctoring to ensure exam integrity. The remote proctoring software monitored the student during the exam through a web camera on their computer/laptop. The recorded video file flags suspicious activities for faculty review after exam completion.

Students’ perceived effectiveness of online learning

Table  1 summarized data on DDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, the majority of courses received a mean score that was approaching or over 3 points on the 4-point scale, suggesting that online learning was generally well accepted by students. Despite overall positive online course experiences, for many of the courses examined, there was an equal split in student responses to the question “ I would have preferred face-to-face instruction for this course .” Additionally, for students’ preferred online delivery method for fully online courses, about half of the students in each class preferred a combination of synchronous and asynchronous online learning (see Fig.  1 ). Finally, the majority of students wanted faculty to continue with some online instruction post pandemic: D1class (110; 78.60 %), D2 class (104; 80 %), and D3 class (49; 83.10 %).

While most online courses received favorable ratings, some variations did exist among courses. For D1 courses, “ Anatomy & Histology ” received lower ratings than others. This could be explained by its lab component, which didn’t lend itself as well to the online format. For D2 courses, several of them received lower ratings than others, especially for the survey question on students’ perceived engagement with faculty and classmates.

DDS students’ preferred online delivery method for fully online courses

Table  2 summarized IDS students’ perceived effectiveness of each online course during summer quarter 2020. For the survey question “ Overall, this online course is effective ”, all courses received a mean score that was approaching or over 3 points on a 4-point scale, suggesting that online learning was well accepted by students. For the survey question “ I would have preferred face-to-face instruction for this course ”, for most online courses examined, the percentage of students who would have preferred face-to-face instruction was similar to that of students who preferred online instruction for the course. Like their DDS peers, about half of the IDS students in each class also preferred a combination of synchronous and asynchronous online delivery for fully online courses (See Fig.  2 ). Finally, the majority of IDS students (I1, n = 18, 81.80 %; I2, n = 16, 84.20 %) wanted to continue with some online learning after the pandemic is over.

IDS students’ preferred online delivery method for fully online courses

Factors impacting students’ acceptance of online learning

For all 19 online courses taken by DDS students, regression analyses indicated that there was a significantly positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across all courses. The ranges of effect size (r 2 ) were: D1 courses (0.26 to 0.50), D2 courses (0.39 to 0.650), and D3 courses (0.22 to 0.44), indicating moderate to high correlations across courses.

For 9 out of the 10 online courses taken by IDS students, there was a positive relationship between students’ perceived engagement with faculty and classmates and their perceived effectiveness of the course. P value was 0.00 across courses. The ranges of effect size were: I1 courses (0.35 to 0.77) and I2 courses (0.47 to 0.63), indicating consistently high correlations across courses. The only course in which students’ perceived engagement with faculty and classmates didn’t predict perceived effective of the course was “ Integrated Clinical Science III (ICS III) ”, which the I2 class took together with their D3 peers.

Impact of online learning on students’ course performance

Chi square test results (Table  3 ) indicated that in 4 out of the 17 courses compared, the online cohort during summer quarter 2020 was more likely to receive an A grade than the face-to-face cohort during summer quarter 2019. In 12 of the courses, the online cohort were equally likely to receive an A grade as the face-to-face cohort. In the remaining one course, the online cohort was less likely to receive an A grade than the face-to-face cohort.

Students’ acceptance of online learning during the pandemic

Survey results revealed that students had generally positive perceptions about online learning during the pandemic and the majority of them wanted to continue with some online learning post pandemic. Overall, our findings supported several other studies in dental [ 18 , 20 ], medical [ 43 , 44 ], and nursing [ 45 ] education that have also reported students’ positive attitudes towards online learning during the pandemic. In their written comments in the survey, students cited enhanced flexibility as one of the greatest benefits of online learning. Some students also commented that typing questions in the chat box during live online classes was less intimidating than speaking in class. Others explicitly stated that not having to commute to/from school provided more time for sleep, which helped with self-care and mental health. Our findings are in line with previous studies which have also demonstrated that online learning offered higher flexibility [ 46 , 47 ]. Meanwhile, consistent with findings of other researchers [ 19 , 21 , 46 ], our students felt difficulty engaging with faculty and classmates in several online courses.

There were some variations among individual courses in students’ acceptance of the online format. One factor that could partially account for the observed differences was instructional strategies. In particular, our regression analysis results demonstrated a positive correlation between students’ perceived engagement with faculty and classmates and their perceived overall effectiveness of the online course. Other aspects of course design might also have influenced students’ overall rating of the online course. For instance, some D2 students commented that the requirements of the course “ Integrated Case-based Seminars (ICS II) ” were not clear and that assessment did not align with lecture materials. It is important to remember that communicating course requirements clearly and aligning course content and assessment are principles that should be applied in any course, whether face-to-face or online. Our results highlighted the importance of providing faculty training on basic educational design principles and online learning design strategies. Furthermore, the nature of the course might also have impacted student ratings. For example, D1 course “ Anatomy and Histology ” had a lab component, which did not lend itself as well to the online format. Many students reported that it was difficult to see faculty’s live demonstration during Zoom lectures, which may have resulted in a lower student satisfaction rating.

As for students’ preferred online delivery method for fully online courses during the pandemic, about half of them preferred a combination of synchronous and asynchronous online learning. In light of this finding, as we continue with remote learning until public health directives allow a return to campus, we will encourage faculty to integrate these two online delivery modalities. Finally, in view of the result that over 80 % of the students wanted to continue with some online instruction after the pandemic, the school will advocate for blended learning in the post-pandemic world [ 48 ]. For future face-to-face courses on campus after the pandemic, faculty are encouraged to deliver some content online to reduce classroom seat time and make learning more flexible. Taken together, our findings not only add to the overall picture of the current situation but may inform learning design moving forward.

Role of online engagement and interaction

To reiterate, we found that students’ perceived engagement with faculty and classmates predicted their perceived overall effectiveness of the online course. This aligns with the larger literature on best practices in online learning design. Extensive research prior to the pandemic has confirmed that the effectiveness of online learning is determined by a number of factors beyond the tools used, including students’ interactions with the instructor and classmates [ 49 , 50 , 51 , 52 ]. Online students may feel isolated due to reduced or lack of interaction [ 53 , 54 ]. Therefore, in designing online learning experiences, it is important to remember that learning is a social process [ 55 ]. Faculty’s role is not only to transmit content but also to promote the different types of interactions that are an integral part of the online learning process [ 33 ]. The online teaching model in which faculty uploads materials online but teach it in the same way as in the physical classroom, without special effort to engage students, doesn’t make the best use of the online format. Putting the “sage on the screen” during a live class meeting on a video conferencing system is not different from “sage on the stage” in the physical classroom - both provide limited space for engagement. Such one-way monologue devalues the potentials that online learning presents.

In light of the critical role that social interaction plays in online learning, faculty are encouraged to use the interactive features of online learning platforms to provide clear channels for student-instructor and student-student interactions. In the open-ended comments, students highlighted several instructional strategies that they perceived to be helpful for learning. For live online classes, these included conducting breakout room activities, using the chat box to facilitate discussions, polling, and integrating gameplay with apps such as Kahoot! @  [ 56 ]. For self-paced classes, students appreciated that faculty held virtual office hours or subsequent live online discussion sessions to reinforce understanding of the pre-assigned materials.

Quality of online education during the pandemic

This study provided empirical evidence in dental education that it was possible to ensure the continuity of education without sacrificing the quality of education provided to students during forced migration to distance learning upon building closure. To reiterate, in all but one online course offered in summer quarter 2020, students were equally or more likely to get an A grade than the face-to-face cohort from summer quarter 2019. Even for courses that had less student support for the online format (e.g., the D1 course “ Anatomy and Histology ”), there was a significant increase in the number of students who earned an A grade in 2020 as compared with the previous year. The reduced capacity for technical training during the pandemic may have resulted in more study time for didactic content. Overall, our results resonate with several studies in health sciences education before the pandemic that the quality of learning is comparable in face-to-face and online formats [ 9 , 57 , 58 ]. For the only course ( Integrated Case-based Seminars ICS II) in which the online cohort had inferior performance than the face-to-face cohort, as mentioned earlier, students reported that assessment was not aligned with course materials and that course expectations were not clear. This might explain why students’ course performance was not as strong as expected.

Limitations

This study used a pre-existing control group from the previous year. There may have been individual differences between students in the online and the face-to-face cohorts, such as motivation, learning style, and prior knowledge, that could have impacted the observed outcomes. Additionally, even though course content and assessment methods were largely the same in 2019 and 2020, changes in other aspects of the course could have impacted students’ course performance. Some faculty may have been more compassionate with grading (e.g., more flexible with assignment deadlines) in summer quarter 2020 given the hardship students experienced during the pandemic. On the other hand, remote proctoring in summer quarter 2020 may have heightened some students’ exam anxiety knowing that they were being monitored through a webcam. The existence and magnitude of effect of these factors needs to be further investigated.

This present study only examined the correlation between students’ perceived online engagement and their perceived overall effectiveness of the online course. Other factors that might impact their acceptance of the online format need to be further researched in future studies. Another future direction is to examine how students’ perceived online engagement correlates with their actual course performance. Because the survey data collected for our present study are anonymous, we cannot match students’ perceived online engagement data with their course grades to run this additional analysis. It should also be noted that this study was focused on didactic online instruction. Future studies might examine how technical training was impacted during the COVID building closure. It was also out of the scope of this study to examine how student characteristics, especially high and low academic performance as reflected by individual grades, affects their online learning experience and performance. We plan to conduct a follow-up study to examine which group of students are most impacted by the online format. Finally, this study was conducted in a single dental school, and so the findings may not be generalizable to other schools and disciplines. Future studies could be conducted in another school or disciplines to compare results.

This study revealed that dental students had generally favorable attitudes towards online learning during the COVID-19 pandemic and that their perceived engagement with faculty and classmates predicted their acceptance of the online course. Most notably, this is the first study in dental education to demonstrate that online learning during the pandemic could achieve similar or better learning outcomes than face-to-face learning before the pandemic. Findings of our study could contribute significantly to the literature on online learning during the COVID-19 pandemic in health sciences education. The results could also inform future online learning design as we re-envision the future of online learning.

Availability of data and materials

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

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MZ is an Associate Professor of Learning Sciences and Senior Instructional Designer at School of Dentistry, University of the Pacific. She has a PhD in Education, with a specialty on learning sciences and technology. She has dedicated her entire career to conducting research on online learning, learning technology, and faculty development. Her research has resulted in several peer-reviewed publications in medical, dental, and educational technology journals. MZ has also presented regularly at national conferences.

DB is an Assistant Dean for Academic Affairs at School of Dentistry, University of the Pacific. He has an EdD degree in education, with a concentration on learning and instruction. Over the past decades, DB has been overseeing and delivering faculty pedagogical development programs to dental faculty. His research interest lies in educational leadership and instructional innovation. DB has co-authored several peer-reviewed publications in health sciences education and presented regularly at national conferences.

CL is Associate Dean of Oral Healthcare Education, School of Dentistry, University of the Pacific. She has a Doctor of Dental Surgery (DDS) degree and an EdD degree with a focus on educational leadership. Her professional interest lies in educational leadership, oral healthcare education innovation, and faculty development. CL has co-authored several publications in peer-reviewed journals in health sciences education and presented regularly at national conferences.

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Zheng, M., Bender, D. & Lyon, C. Online learning during COVID-19 produced equivalent or better student course performance as compared with pre-pandemic: empirical evidence from a school-wide comparative study. BMC Med Educ 21 , 495 (2021). https://doi.org/10.1186/s12909-021-02909-z

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A thesis statement is a sentence that sums up the central point of your paper or essay . Everything else you write should relate to this key idea.

The thesis statement is essential in any academic essay or research paper for two main reasons:

• It gives your writing direction and focus.
• It gives the reader a concise summary of your main point.

Without a clear thesis statement, an essay can end up rambling and unfocused, leaving your reader unsure of exactly what you want to say.

Follow these four steps to come up with a thesis statement :

• Ask a question about your topic .
• Write your initial answer.
• Develop your answer by including reasons.
• Refine your answer, adding more detail and nuance.

The thesis statement should be placed at the end of your essay introduction .

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McCombes, S. (2023, August 15). How to Write a Thesis Statement | 4 Steps & Examples. Scribbr. Retrieved March 18, 2024, from https://www.scribbr.com/academic-essay/thesis-statement/

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