Advertisement

Advertisement

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

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

Cite this article

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

75k Accesses

22 Citations

11 Altmetric

Explore all metrics

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.

Avoid common mistakes on your manuscript.

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 .

figure 1

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

figure 2

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

figure 3

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

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

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

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

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

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

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

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

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

4 Conclusions and discussion

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

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

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

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

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

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

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

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

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

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

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

*Studies included in meta-analysis

Ahmad, S., Sumardi, K., & Purnawan, P. (2016). Komparasi Peningkatan Hasil Belajar Antara Pembelajaran Menggunakan Sistem Pembelajaran Online Terpadu Dengan Pembelajaran Klasikal Pada Mata Kuliah Pneumatik Dan Hidrolik. Journal of Mechanical Engineering Education, 2 (2), 286–292.

Article   Google Scholar  

Ally, M. (2004). Foundations of educational theory for online learning. Theory and Practice of Online Learning, 2 , 15–44. Retrieved on the 11th of September, 2020 from https://eddl.tru.ca/wp-content/uploads/2018/12/01_Anderson_2008-Theory_and_Practice_of_Online_Learning.pdf

Arat, T., & Bakan, Ö. (2011). Uzaktan eğitim ve uygulamaları. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksek Okulu Dergisi , 14 (1–2), 363–374. https://doi.org/10.29249/selcuksbmyd.540741

Astuti, C. C., Sari, H. M. K., & Azizah, N. L. (2019). Perbandingan Efektifitas Proses Pembelajaran Menggunakan Metode E-Learning dan Konvensional. Proceedings of the ICECRS, 2 (1), 35–40.

*Atici, B., & Polat, O. C. (2010). Influence of the online learning environments and tools on the student achievement and opinions. Educational Research and Reviews, 5 (8), 455–464. Retrieved on the 11th of October, 2020 from https://academicjournals.org/journal/ERR/article-full-text-pdf/4C8DD044180.pdf

Bernard, R. M., Abrami, P. C., Lou, Y., Borokhovski, E., Wade, A., Wozney, L., et al. (2004). How does distance education compare with classroom instruction? A meta- analysis of the empirical literature. Review of Educational Research, 3 (74), 379–439. https://doi.org/10.3102/00346543074003379

Borenstein, M., Hedges, L. V., Higgins, J. P. T., & Rothstein, H. R. (2009). Introduction to meta-analysis . Wiley.

Book   Google Scholar  

Borenstein, M., Hedges, L., & Rothstein, H. (2007). Meta-analysis: Fixed effect vs. random effects . UK: Wiley.

Card, N. A. (2011). Applied meta-analysis for social science research: Methodology in the social sciences . Guilford.

Google Scholar  

*Carreon, J. R. (2018 ). Facebook as integrated blended learning tool in technology and livelihood education exploratory. Retrieved on the 1st of October, 2020 from https://files.eric.ed.gov/fulltext/EJ1197714.pdf

Cavanaugh, C., Gillan, K. J., Kromrey, J., Hess, M., & Blomeyer, R. (2004). The effects of distance education on K-12 student outcomes: A meta-analysis. Learning Point Associates/North Central Regional Educational Laboratory (NCREL) . Retrieved on the 11th of September, 2020 from https://files.eric.ed.gov/fulltext/ED489533.pdf

*Ceylan, V. K., & Elitok Kesici, A. (2017). Effect of blended learning to academic achievement. Journal of Human Sciences, 14 (1), 308. https://doi.org/10.14687/jhs.v14i1.4141

*Chae, S. E., & Shin, J. H. (2016). Tutoring styles that encourage learner satisfaction, academic engagement, and achievement in an online environment. Interactive Learning Environments, 24(6), 1371–1385. https://doi.org/10.1080/10494820.2015.1009472

*Chiang, T. H. C., Yang, S. J. H., & Hwang, G. J. (2014). An augmented reality-based mobile learning system to improve students’ learning achievements and motivations in natural science inquiry activities. Educational Technology and Society, 17 (4), 352–365. Retrieved on the 11th of September, 2020 from https://www.researchgate.net/profile/Gwo_Jen_Hwang/publication/287529242_An_Augmented_Reality-based_Mobile_Learning_System_to_Improve_Students'_Learning_Achievements_and_Motivations_in_Natural_Science_Inquiry_Activities/links/57198c4808ae30c3f9f2c4ac.pdf

Chiao, H. M., Chen, Y. L., & Huang, W. H. (2018). Examining the usability of an online virtual tour-guiding platform for cultural tourism education. Journal of Hospitality, Leisure, Sport & Tourism Education, 23 (29–38), 1. https://doi.org/10.1016/j.jhlste.2018.05.002

Chizmar, J. F., & Walbert, M. S. (1999). Web-based learning environments guided by principles of good teaching practice. Journal of Economic Education, 30 (3), 248–264. https://doi.org/10.2307/1183061

Cleophas, T. J., & Zwinderman, A. H. (2017). Modern meta-analysis: Review and update of methodologies . Switzerland: Springer. https://doi.org/10.1007/978-3-319-55895-0

Cohen, L., Manion, L., & Morrison, K. (2007). Observation.  Research Methods in Education, 6 , 396–412. Retrieved on the 11th of September, 2020 from https://www.researchgate.net/profile/Nabil_Ashraf2/post/How_to_get_surface_potential_Vs_Voltage_curve_from_CV_and_GV_measurements_of_MOS_capacitor/attachment/5ac6033cb53d2f63c3c405b4/AS%3A612011817844736%401522926396219/download/Very+important_C-V+characterization+Lehigh+University+thesis.pdf

Colis, B., & Moonen, J. (2001). Flexible Learning in a Digital World: Experiences and Expectations. Open & Distance Learning Series . Stylus Publishing.

CoSN. (2020). COVID-19 Response: Preparing to Take School Online. CoSN. (2020). COVID-19 Response: Preparing to Take School Online. Retrieved on the 3rd of September, 2021 from https://www.cosn.org/sites/default/files/COVID-19%20Member%20Exclusive_0.pdf

Cumming, G. (2012). Understanding new statistics: Effect sizes, confidence intervals, and meta-analysis. New York, USA: Routledge. https://doi.org/10.4324/9780203807002

Deeks, J. J., Higgins, J. P. T., & Altman, D. G. (2008). Analysing data and undertaking meta-analyses . In J. P. T. Higgins & S. Green (Eds.), Cochrane handbook for systematic reviews of interventions (pp. 243–296). Sussex: John Wiley & Sons. https://doi.org/10.1002/9780470712184.ch9

Demiralay, R., Bayır, E. A., & Gelibolu, M. F. (2016). Öğrencilerin bireysel yenilikçilik özellikleri ile çevrimiçi öğrenmeye hazır bulunuşlukları ilişkisinin incelenmesi. Eğitim ve Öğretim Araştırmaları Dergisi, 5 (1), 161–168. https://doi.org/10.23891/efdyyu.2017.10

Dinçer, S. (2014). Eğitim bilimlerinde uygulamalı meta-analiz. Pegem Atıf İndeksi, 2014(1), 1–133. https://doi.org/10.14527/pegem.001

*Durak, G., Cankaya, S., Yunkul, E., & Ozturk, G. (2017). The effects of a social learning network on students’ performances and attitudes. European Journal of Education Studies, 3 (3), 312–333. 10.5281/zenodo.292951

*Ercan, O. (2014). Effect of web assisted education supported by six thinking hats on students’ academic achievement in science and technology classes . European Journal of Educational Research, 3 (1), 9–23. https://doi.org/10.12973/eu-jer.3.1.9

Ercan, O., & Bilen, K. (2014). Effect of web assisted education supported by six thinking hats on students’ academic achievement in science and technology classes. European Journal of Educational Research, 3 (1), 9–23.

*Ercan, O., Bilen, K., & Ural, E. (2016). “Earth, sun and moon”: Computer assisted instruction in secondary school science - Achievement and attitudes. Issues in Educational Research, 26 (2), 206–224. https://doi.org/10.12973/eu-jer.3.1.9

Field, A. P. (2003). The problems in using fixed-effects models of meta-analysis on real-world data. Understanding Statistics, 2 (2), 105–124. https://doi.org/10.1207/s15328031us0202_02

Field, A. P., & Gillett, R. (2010). How to do a meta-analysis. British Journal of Mathematical and Statistical Psychology, 63 (3), 665–694. https://doi.org/10.1348/00071010x502733

Geostat. (2019). ‘Share of households with internet access’, National statistics office of Georgia . Retrieved on the 2nd September 2020 from https://www.geostat.ge/en/modules/categories/106/information-and-communication-technologies-usage-in-households

*Gwo-Jen, H., Nien-Ting, T., & Xiao-Ming, W. (2018). Creating interactive e-books through learning by design: The impacts of guided peer-feedback on students’ learning achievements and project outcomes in science courses. Journal of Educational Technology & Society., 21 (1), 25–36. Retrieved on the 2nd of October, 2020 https://ae-uploads.uoregon.edu/ISTE/ISTE2019/PROGRAM_SESSION_MODEL/HANDOUTS/112172923/CreatingInteractiveeBooksthroughLearningbyDesignArticle2018.pdf

Hamdani, A. R., & Priatna, A. (2020). Efektifitas implementasi pembelajaran daring (full online) dimasa pandemi Covid-19 pada jenjang Sekolah Dasar di Kabupaten Subang. Didaktik: Jurnal Ilmiah PGSD STKIP Subang, 6 (1), 1–9.

Hart, C. M., Berger, D., Jacob, B., Loeb, S., & Hill, M. (2019). Online learning, offline outcomes: Online course taking and high school student performance. Aera Open, 5(1).

*Hayes, J., & Stewart, I. (2016). Comparing the effects of derived relational training and computer coding on intellectual potential in school-age children. The British Journal of Educational Psychology, 86 (3), 397–411. https://doi.org/10.1111/bjep.12114

Horton, W. K. (2000). Designing web-based training: How to teach anyone anything anywhere anytime (Vol. 1). Wiley Publishing.

*Hwang, G. J., Wu, P. H., & Chen, C. C. (2012). An online game approach for improving students’ learning performance in web-based problem-solving activities. Computers and Education, 59 (4), 1246–1256. https://doi.org/10.1016/j.compedu.2012.05.009

*Kert, S. B., Köşkeroğlu Büyükimdat, M., Uzun, A., & Çayiroğlu, B. (2017). Comparing active game-playing scores and academic performances of elementary school students. Education 3–13, 45 (5), 532–542. https://doi.org/10.1080/03004279.2016.1140800

*Lai, A. F., & Chen, D. J. (2010). Web-based two-tier diagnostic test and remedial learning experiment. International Journal of Distance Education Technologies, 8 (1), 31–53. https://doi.org/10.4018/jdet.2010010103

*Lai, A. F., Lai, H. Y., Chuang W. H., & Wu, Z.H. (2015). Developing a mobile learning management system for outdoors nature science activities based on 5e learning cycle. Proceedings of the International Conference on e-Learning, ICEL. Proceedings of the International Association for Development of the Information Society (IADIS) International Conference on e-Learning (Las Palmas de Gran Canaria, Spain, July 21–24, 2015). Retrieved on the 14th November 2020 from https://files.eric.ed.gov/fulltext/ED562095.pdf

Lai, C. H., Lin, H. W., Lin, R. M., & Tho, P. D. (2019). Effect of peer interaction among online learning community on learning engagement and achievement. International Journal of Distance Education Technologies (IJDET), 17 (1), 66–77.

Littell, J. H., Corcoran, J., & Pillai, V. (2008). Systematic reviews and meta-analysis . Oxford University.

*Liu, K. P., Tai, S. J. D., & Liu, C. C. (2018). Enhancing language learning through creation: the effect of digital storytelling on student learning motivation and performance in a school English course. Educational Technology Research and Development, 66 (4), 913–935. https://doi.org/10.1007/s11423-018-9592-z

Machtmes, K., & Asher, J. W. (2000). A meta-analysis of the effectiveness of telecourses in distance education. American Journal of Distance Education, 14 (1), 27–46. https://doi.org/10.1080/08923640009527043

Makowski, D., Piraux, F., & Brun, F. (2019). From experimental network to meta-analysis: Methods and applications with R for agronomic and environmental sciences. Dordrecht: Springer. https://doi.org/10.1007/978-94-024_1696-1

* Meyers, C., Molefe, A., & Brandt, C. (2015). The Impact of the" Enhancing Missouri's Instructional Networked Teaching Strategies"(eMINTS) Program on Student Achievement, 21st-Century Skills, and Academic Engagement--Second-Year Results . Society for Research on Educational Effectiveness. Retrieved on the 14 th November, 2020 from https://files.eric.ed.gov/fulltext/ED562508.pdf

OECD. (2020). ‘A framework to guide an education response to the COVID-19 Pandemic of 2020 ’. https://doi.org/10.26524/royal.37.6

Pecoraro, V. (2018). Appraising evidence . In G. Biondi-Zoccai (Ed.), Diagnostic meta-analysis: A useful tool for clinical decision-making (pp. 99–114). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-78966-8_9

Pigott, T. (2012). Advances in meta-analysis . Springer.

Pillay, H. , Irving, K., & Tones, M. (2007). Validation of the diagnostic tool for assessing Tertiary students’ readiness for online learning. Higher Education Research & Development, 26 (2), 217–234. https://doi.org/10.1080/07294360701310821

Prestiadi, D., Zulkarnain, W., & Sumarsono, R. B. (2019). Visionary leadership in total quality management: efforts to improve the quality of education in the industrial revolution 4.0. In the 4th International Conference on Education and Management (COEMA 2019). Atlantis Press

Poole, D. M. (2000). Student participation in a discussion-oriented online course: a case study. Journal of Research on Computing in Education, 33 (2), 162–177. https://doi.org/10.1080/08886504.2000.10782307

Rahayu, F. S., Budiyanto, D., & Palyama, D. (2017). Analisis penerimaan e-learning menggunakan technology acceptance model (Tam)(Studi Kasus: Universitas Atma Jaya Yogyakarta). Jurnal Terapan Teknologi Informasi, 1 (2), 87–98.

Rasmussen, R. C. (2003). The quantity and quality of human interaction in a synchronous blended learning environment . Brigham Young University Press.

*Ravenel, J., T. Lambeth, D., & Spires, B. (2014). Effects of computer-based programs on mathematical achievement scores for fourth-grade students. i-manager’s Journal on School Educational Technology, 10 (1), 8–21. https://doi.org/10.26634/jsch.10.1.2830

Rolisca, R. U. C., & Achadiyah, B. N. (2014). Pengembangan media evaluasi pembelajaran dalam bentuk online berbasis e-learning menggunakan software wondershare quiz creator dalam mata pelajaran akuntansi SMA Brawijaya Smart School (BSS). Jurnal Pendidikan Akuntansi Indonesia, 12(2).

Sitzmann, T., Kraiger, K., Stewart, D., & Wisher, R. (2006). The comparative effective- ness of Web-based and classroom instruction: A meta-analysis . Personnel Psychology, 59 (3), 623–664. https://doi.org/10.1111/j.1744-6570.2006.00049.x

Stewart, D. W., & Kamins, M. A. (2001). Developing a coding scheme and coding study reports. In M. W. Lipsey & D. B. Wilson (Eds.), Practical meta­analysis: Applied social research methods series (Vol. 49, pp. 73–90). Sage.

Swan, K. (2007). Research on online learning. Journal of Asynchronous Learning Networks, 11 (1), 55–59.

*Sung, H. Y., Hwang, G. J., & Chang, Y. C. (2016). Development of a mobile learning system based on a collaborative problem-posing strategy. Interactive Learning Environments, 24 (3), 456–471. https://doi.org/10.1080/10494820.2013.867889

Tsagris, M., & Fragkos, K. C. (2018). Meta-analyses of clinical trials versus diagnostic test accuracy studies. In G. Biondi-Zoccai (Ed.), Diagnostic meta-analysis: A useful tool for clinical decision-making (pp. 31–42). Cham, Switzerland: Springer. https://doi.org/10.1007/978-3-319-78966-8_4

UNESCO. (2020, Match 13). COVID-19 educational disruption and response. Retrieved on the 14 th November 2020 from https://en.unesco.org/themes/education-emergencies/ coronavirus-school-closures

Usta, E. (2011a). The effect of web-based learning environments on attitudes of students regarding computer and internet. Procedia-Social and Behavioral Sciences, 28 (262–269), 1. https://doi.org/10.1016/j.sbspro.2011.11.051

Usta, E. (2011b). The examination of online self-regulated learning skills in web-based learning environments in terms of different variables. Turkish Online Journal of Educational Technology-TOJET, 10 (3), 278–286. Retrieved on the 14th November 2020 from https://files.eric.ed.gov/fulltext/EJ944994.pdf

Vrasidas, C. & MsIsaac, M. S. (2000). Principles of pedagogy and evaluation for web-based learning. Educational Media International, 37 (2), 105–111. https://doi.org/10.1080/095239800410405

*Wang, C. H., & Chen, C. P. (2013). Effects of facebook tutoring on learning english as a second language. Proceedings of the International Conference e-Learning 2013, (2009), 135–142. Retrieved on the 15th November 2020 from https://files.eric.ed.gov/fulltext/ED562299.pdf

Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41 (1), 48–69.

*Yu, F. Y. (2019). The learning potential of online student-constructed tests with citing peer-generated questions. Interactive Learning Environments, 27 (2), 226–241. https://doi.org/10.1080/10494820.2018.1458040

*Yu, F. Y., & Chen, Y. J. (2014). Effects of student-generated questions as the source of online drill-and-practice activities on learning . British Journal of Educational Technology, 45 (2), 316–329. https://doi.org/10.1111/bjet.12036

*Yu, F. Y., & Pan, K. J. (2014). The effects of student question-generation with online prompts on learning. Educational Technology and Society, 17 (3), 267–279. Retrieved on the 15th November 2020 from http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.565.643&rep=rep1&type=pdf

*Yu, W. F., She, H. C., & Lee, Y. M. (2010). The effects of web-based/non-web-based problem-solving instruction and high/low achievement on students’ problem-solving ability and biology achievement. Innovations in Education and Teaching International, 47 (2), 187–199. https://doi.org/10.1080/14703291003718927

Zhao, Y., Lei, J., Yan, B, Lai, C., & Tan, S. (2005). A practical analysis of research on the effectiveness of distance education. Teachers College Record, 107 (8). https://doi.org/10.1111/j.1467-9620.2005.00544.x

*Zhong, B., Wang, Q., Chen, J., & Li, Y. (2017). Investigating the period of switching roles in pair programming in a primary school. Educational Technology and Society, 20 (3), 220–233. Retrieved on the 15th November 2020 from https://repository.nie.edu.sg/bitstream/10497/18946/1/ETS-20-3-220.pdf

Download references

Author information

Authors and affiliations.

Primary Education, Ministry of Turkish National Education, Mersin, Turkey

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Hakan Ulum .

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

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

Download citation

Received : 06 December 2020

Accepted : 30 August 2021

Published : 06 September 2021

Issue Date : January 2022

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

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

  • Online education
  • Student achievement
  • Academic success
  • Meta-analysis
  • Find a journal
  • Publish with us
  • Track your research

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

effects of online classes essay

  • Share article

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

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

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

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

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

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

Coronavirus and Schools

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

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

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

About this series

BRIC ARCHIVE

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

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

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

Read the full series here .

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

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

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

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

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

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

Sign Up for EdWeek Tech Leader

Edweek top school jobs.

Photo illustration of student with laptop.

Sign Up & Sign In

module image 9

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • View all journals
  • Explore content
  • About the journal
  • Publish with us
  • Sign up for alerts
  • Published: 25 January 2021

Online education in the post-COVID era

  • Barbara B. Lockee 1  

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

136k Accesses

197 Citations

337 Altmetric

Metrics details

  • Science, technology and society

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

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

effects of online classes essay

Looking back

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

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

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

Online learning and the pandemic

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

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

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

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

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

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

Mackey, J., Gilmore, F., Dabner, N., Breeze, D. & Buckley, P. J. Online Learn. Teach. 8 , 35–48 (2012).

Google Scholar  

Sands, T. & Shushok, F. The COVID-19 higher education shove. Educause Review https://go.nature.com/3o2vHbX (16 October 2020).

Hodges, C., Moore, S., Lockee, B., Trust, T. & Bond, M. A. The difference between emergency remote teaching and online learning. Educause Review https://go.nature.com/38084Lh (27 March 2020).

Beatty, B. J. (ed.) Hybrid-Flexible Course Design Ch. 1.4 https://go.nature.com/3o6Sjb2 (EdTech Books, 2019).

Skinner, B. F. Science 128 , 969–977 (1958).

Article   Google Scholar  

Keller, F. S. J. Appl. Behav. Anal. 1 , 79–89 (1968).

Darling-Hammond, L. et al. Restarting and Reinventing School: Learning in the Time of COVID and Beyond (Learning Policy Institute, 2020).

Fulton, C. Information Learn. Sci . 121 , 579–585 (2020).

Pennisi, E. Science 369 , 239–240 (2020).

Silva, E. & White, T. Change The Magazine Higher Learn. 47 , 68–72 (2015).

McIsaac, M. S. & Gunawardena, C. N. in Handbook of Research for Educational Communications and Technology (ed. Jonassen, D. H.) Ch. 13 (Simon & Schuster Macmillan, 1996).

Irvine, V. The landscape of merging modalities. Educause Review https://go.nature.com/2MjiBc9 (26 October 2020).

Stein, J. & Graham, C. Essentials for Blended Learning Ch. 1 (Routledge, 2020).

Maloy, R. W., Trust, T. & Edwards, S. A. Variety is the spice of remote learning. Medium https://go.nature.com/34Y1NxI (24 August 2020).

Lockee, B. J. Appl. Instructional Des . https://go.nature.com/3b0ddoC (2020).

Dunlap, J. & Lowenthal, P. Open Praxis 10 , 79–89 (2018).

Johnson, N., Veletsianos, G. & Seaman, J. Online Learn. 24 , 6–21 (2020).

Vaughan, N. D., Cleveland-Innes, M. & Garrison, D. R. Assessment in Teaching in Blended Learning Environments: Creating and Sustaining Communities of Inquiry (Athabasca Univ. Press, 2013).

Conrad, D. & Openo, J. Assessment Strategies for Online Learning: Engagement and Authenticity (Athabasca Univ. Press, 2018).

Download references

Author information

Authors and affiliations.

School of Education, Virginia Tech, Blacksburg, VA, USA

Barbara B. Lockee

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Barbara B. Lockee .

Ethics declarations

Competing interests.

The author declares no competing interests.

Rights and permissions

Reprints and permissions

About this article

Cite this article.

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

Download citation

Published : 25 January 2021

Issue Date : January 2021

DOI : https://doi.org/10.1038/s41928-020-00534-0

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

This article is cited by

A comparative study on the effectiveness of online and in-class team-based learning on student performance and perceptions in virtual simulation experiments.

BMC Medical Education (2024)

Leveraging privacy profiles to empower users in the digital society

  • Davide Di Ruscio
  • Paola Inverardi
  • Phuong T. Nguyen

Automated Software Engineering (2024)

Nursing students’ learning flow, self-efficacy and satisfaction in virtual clinical simulation and clinical case seminar

  • Sunghee H. Tak

BMC Nursing (2023)

Online learning for WHO priority diseases with pandemic potential: evidence from existing courses and preparing for Disease X

  • Heini Utunen
  • Corentin Piroux

Archives of Public Health (2023)

Exploring educational impacts among pre, during and post COVID-19 lockdowns from students with different personality traits

  • Shuaiqi Zheng

International Journal of Educational Technology in Higher Education (2023)

Quick links

  • Explore articles by subject
  • Guide to authors
  • Editorial policies

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

effects of online classes essay

  • India Today
  • Business Today
  • Reader’s Digest
  • Harper's Bazaar
  • Brides Today
  • Cosmopolitan
  • Aaj Tak Campus
  • India Today Hindi

effects of online classes essay

Effects of online education on mental and physical health

Online education has drastically changed the way we study but the year and half of attending online classes from home have led to a string of mental and physical health issues for both students and teachers..

Listen to Story

Effects of online education on mental and physical health

Covid-19 has caused destruction and devastation worldwide in ways nobody could anticipate. The world in one way or another came to a standstill. Life as we knew it changed. And this change became the new constant.

Educational institutions took to online teaching. The start of this change felt rather very enticing for the students with not having to rush and get ready to reach the institutions, and being in the comfort of their homes.

How online education affected mental health

1. Lack of interest

Humans are social animals, and the most introverted ones also need to see faces and have human interactions once in a while. The children have grown to lose interest in their classes.

Most of them switch off the camera and go about their other activities. The lethargy has inculcated the loss of interest in not only the studies but everything overall.

The pressure of after-school homework and assignments has triggered a great toll on the mental health and mood.

2. Stress and anxiety

The concentration levels of students dropped in online learning as the eye meanders elsewhere on the screen. This in response made it difficult for most students to keep up with the teachings.

The pressure to concentrate and produce the required results has resulted in a great amount of stress and anxiety. Tasks, assignments, and homework slacked.

Most children were seen lagging behind and succumbing to the pressure. The mental state of the children was fragile and tampered with.

3. Zoom fatigue

Zoom fatigue refers to the exhaustion after having attended zoom classes, or video conferences. With the screen time increasing drastically, the mind is overwhelmed with information and the brain finds it rather difficult to register all the information.

effects of online classes essay

The pros and cons of online learning

What to look for in an online course.

By: MIT xPRO

If you’re at a point in your life where you’re considering continuing your education, you may wonder if online learning is the right path for you.

Taking an online course requires a notable investment of time, effort, and money, so it’s important to feel confident about your decision before moving forward. While online learning works incredibly well for some people, it’s not for everyone.

We recently sat down with MIT xPRO Senior Instructional Designer and Program Manager Luke Hobson to explore the pros and cons of online learning and what to look for in an online course. If you’re waiting for a sign about whether or not to enroll in that course you’ve been eying, you just might find it here.

Pros of Online Learning

First, let’s take a look at the true value of online learning by examining some of the benefits:

1. Flexibility

Online learning’s most significant advantage is its flexibility. It’s the reason millions of adults have chosen to continue their education and pursue certificates and degrees.

Asynchronous courses allow learners to complete work at their own pace, empowering them to find the optimal time to consume the content and submit assignments.

Some people are more attentive, focused, and creative in the mornings compared to the evenings and vice versa. Whatever works best for the learners should be the priority of the learning experience.

2. Community

When Luke asks people about their main reason for enrolling in a course, a common answer is networking and community.

Learners crave finding like-minded individuals who are going through the same experiences and have the same questions. They want to find a place where they belong. Being in the company of others who understand what they’re going through can help online learners who are looking for support and motivation during challenging times and times that are worth celebrating.

Some learners have created study groups and book clubs that have carried on far beyond the end of the course-it’s amazing what can grow from a single post on a discussion board!

3. Latest information

“Speed is a massive benefit of online learning,” and according to Luke, it often doesn’t get the attention it deserves.

“When we say speed, we don’t mean being quick with learning. We mean actual speed to market. There are so many new ideas evolving within technical spaces that it’s impossible to keep courses the way they were originally designed for a long period of time.”

Luke notes that a program on Additive Manufacturing , Virtual Reality and Augmented Reality , or Nanotechnology must be checked and updated frequently. More formal learning modalities have difficulty changing content at this rapid pace. But within the online space, it’s expected that the course content will change as quickly as the world itself does.

Cons of Online Learning

Now that we’ve looked at some of the biggest pros of online learning, let’s examine a few of the drawbacks:

1. Learning environment

While many learners thrive in an asynchronous learning environment, others struggle. Some learners prefer live lessons and an instructor they can connect with multiple times a week. They need these interactions to feel supported and to persist.

Most learners within the online space identify themselves as self-directed learners, meaning they can learn on their own with the right environment, guidance, materials, and assignments. Learners should know themselves first and understand their preferences when it comes to what kind of environment will help them thrive.

2. Repetition

One drawback of online courses is that the structure can be repetitive: do a reading, respond to two discussion posts, submit an essay, repeat. After a while, some learners may feel disengaged from the learning experience.

There are online courses that break the mold and offer multiple kinds of learning activities, assessments, and content to make the learning experience come alive, but it may take some research to find them-more on what to look for in an online course later in this article! Luke and his colleagues at MIT xPRO are mindful of designing courses that genuinely engage learners from beginning to end.

3. Underestimation

Luke has noticed that some learners underestimate how much work is required in an online course. They may mistakenly believe that online learning is somehow “easier” compared to in-person learning.

For those learners who miscalculate how long they will need to spend online or how challenging the assignments can be, changing that mindset is a difficult process. It’s essential to set aside the right amount of time per week to contribute to the content, activities, and assignments. Creating personal deadlines and building a study routine are two best practices that successful online learners follow to hold themselves accountable.

Experience the Value of Online Learning: What to Look For in an Online Course

You’ve probably gathered by now that not all online courses are created equal. On one end of the spectrum, there are methods of online learning that leave learners stunned by what a great experience they had. On the other end of the spectrum, some online learning courses are so disappointing that learners regret their decision to enroll.

If you want to experience the value of online learning, it’s essential to pick the right course. Here’s a quick list of what to look for:

  • Feedback and connection to peers within the course platform. Interacting regularly with other learners makes a big difference. Luke and the MIT xPRO team use peer-reviewed feedback to give learners the opportunity to engage with each other’s work.
  • Proof of hard work. In the online learning space, proof of hard work often comes in the form of Continuing Education Units (CEUs) or specific certifications. MIT xPRO course participants who successfully complete one or more courses are eligible to receive CEUs , which many employers, licensing agencies, and professional associations accept as evidence of a participant’s serious commitment to their professional development.

Online learning isn’t for everyone, but with the right approach, it can be a valuable experience for many people. Now that you know what to look for in an online course, see what Luke and the MIT xPRO instructional design team have to offer by checking out the latest MIT xPRO courses and programs .

Originally published at http://curve.mit.edu on August 8th, 2022.

effects of online classes essay

The pros and cons of online learning was originally published in MIT Open Learning on Medium, where people are continuing the conversation by highlighting and responding to this story.

Open Learning newsletter

effects of online classes essay

25,000+ students realised their study abroad dream with us. Take the first step today

Meet top uk universities from the comfort of your home, here’s your new year gift, one app for all your, study abroad needs, start your journey, track your progress, grow with the community and so much more.

effects of online classes essay

Verification Code

An OTP has been sent to your registered mobile no. Please verify

effects of online classes essay

Thanks for your comment !

Our team will review it before it's shown to our readers.

Leverage Edu

  • School Education /

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

' src=

  • Updated on  
  • Oct 20, 2023

Essay on Online Classes

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

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

This Blog Includes:

What are online classes, essay on online classes in 100 words, essay on online classes in 150 words, essay on online classes in 200 words.

Also Read: Online Courses

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

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

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

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

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

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

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

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

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

Also Read: Online Learning

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

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

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

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

Related Articles

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

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

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

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

' src=

Malvika Chawla

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

Leave a Reply Cancel reply

Save my name, email, and website in this browser for the next time I comment.

Contact no. *

effects of online classes essay

Connect With Us

effects of online classes essay

25,000+ students realised their study abroad dream with us. Take the first step today.

effects of online classes essay

Resend OTP in

effects of online classes essay

Need help with?

Study abroad.

UK, Canada, US & More

IELTS, GRE, GMAT & More

Scholarship, Loans & Forex

Country Preference

New Zealand

Which English test are you planning to take?

Which academic test are you planning to take.

Not Sure yet

When are you planning to take the exam?

Already booked my exam slot

Within 2 Months

Want to learn about the test

Which Degree do you wish to pursue?

When do you want to start studying abroad.

January 2024

September 2024

What is your budget to study abroad?

effects of online classes essay

How would you describe this article ?

Please rate this article

We would like to hear more.

Have something on your mind?

effects of online classes essay

Make your study abroad dream a reality in January 2022 with

effects of online classes essay

India's Biggest Virtual University Fair

effects of online classes essay

Essex Direct Admission Day

Why attend .

effects of online classes essay

Don't Miss Out

Online Classes Essay

effects of online classes essay

Introduction

Modern technology is revolutionising the way education is delivered. More and more universities and schools have started adopting online learning as a regular part of their academic activities due to many advantages. This transition was speeded up since the Covid-19 pandemic brought education to a standstill across the world. In this essay let us explore the different aspects regarding online education.

Online Classes

We are familiar with traditional learning, which involves schools, classrooms, teachers and students. The learning that happens online with the help of the internet is referred to as online education. Also, teachers need not be physically present with them. With the help of a computer or smartphone, students can learn from the comfort of their homes. In this online classes essay in English, we will understand how the pandemic has affected the educational sector and online learning has benefited children.

Benefits of Online Classes

We might know that some students have to travel long distances to reach their schools and the time required for them to study is mostly lost in travelling. Online education addresses this serious drawback by bringing education to where students are rather than forcing them to come to school. Even if children are in a different state or country due to some reason, they don’t have to be worried about missing any classes. Based on their convenience and time, children can schedule their classes online. We will further see its impact in this short essay on online classes.

With the launch of online classes, children will have less dependency on books and photocopies, as notes and assignments are shared online. Besides, teachers find it easy to teach them online through informative videos and images. Children will be able to grasp the concepts more effectively through visual learning. Children can also connect with teachers regularly and better understand the subjects. Thus, online classes ensure that children get a quality education without worrying about time and place.

Just like a coin has two sides, there are also positive and negative effects of online classes. But, the advantages outweigh limited interactions, and this is why online classes have a huge impact in this period. By overcoming the limitations, online classes can reach a wider audience, and it has the potential to bring about a change in the education system. Like this short essay on online classes, you can find numerous essays , stories, poems, worksheets and GK questions for your children on our website.

Frequently Asked Questions on Online Classes Essay

What are the main disadvantages of online classes.

Online classes cannot be possible in areas where there is poor internet connectivity, and children do not have access to laptops or computers. While the interaction between students becomes minimal, online classes also pose the threat of increased screen time, thus affecting the children badly.

Are online classes effective in children?

Online classes are found to be effective and useful for children, as they can plan their studies based on their convenience and time. They will also be able to better balance academics and other activities finely through online classes, which makes them more creative and intuitive.

Leave a Comment Cancel reply

Your Mobile number and Email id will not be published. Required fields are marked *

Request OTP on Voice Call

Post My Comment

effects of online classes essay

  • Share Share

Register with BYJU'S & Download Free PDFs

Register with byju's & watch live videos.

Online Classes Vs. Traditional Classes Essay

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

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

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

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

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

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

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

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

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

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

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

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

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

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

  • Chicago (A-D)
  • Chicago (N-B)

IvyPanda. (2023, October 28). Online Classes Vs. Traditional Classes Essay. https://ivypanda.com/essays/online-classes-vs-traditional-classes-essay/

"Online Classes Vs. Traditional Classes Essay." IvyPanda , 28 Oct. 2023, ivypanda.com/essays/online-classes-vs-traditional-classes-essay/.

IvyPanda . (2023) 'Online Classes Vs. Traditional Classes Essay'. 28 October.

IvyPanda . 2023. "Online Classes Vs. Traditional Classes Essay." October 28, 2023. https://ivypanda.com/essays/online-classes-vs-traditional-classes-essay/.

1. IvyPanda . "Online Classes Vs. Traditional Classes Essay." October 28, 2023. https://ivypanda.com/essays/online-classes-vs-traditional-classes-essay/.

Bibliography

IvyPanda . "Online Classes Vs. Traditional Classes Essay." October 28, 2023. https://ivypanda.com/essays/online-classes-vs-traditional-classes-essay/.

  • Technology Ruins One-on-One Interaction and Relations
  • Remote vs In-person Classes: Positive and Negative Aspects
  • Mathematics Teaching Approaches in Burns' Study
  • The Impressions of Emirati Youths on ISIS
  • Managers’ Training Proposal
  • Coaching Approaches. Separation by Target and Source Data.
  • Walmart Workplace Aspects Analysis
  • The Value of In-Person Human Interaction
  • Organizational Behavior and Workplace Conflicts
  • Educator Ethics: The Case Study
  • Principles Application in E-Learning
  • Cambourne’s Conditions of Learning
  • Concept of Transformative Learning in Modern Education
  • Podcasts as an Education Tool
  • Wikis as an Educational Tool

The Causal Effect of Parents’ Education on Children’s Earnings

We present a model of endogenous schooling and earnings to isolate the causal effect of parents’ education on children’s education and earnings outcomes. The model suggests that parents’ education is positively related to children’s earnings, but its relationship with children’s education is ambiguous. Identification is achieved by comparing the earnings of children with the same length of schooling, whose parents have different lengths of schooling. The model also features heterogeneous preferences for schooling, and is estimated using HRS data. The empirically observed positive OLS coefficient obtained by regressing children’s schooling on parents’ schooling is mainly accounted for by the correlation between parents’ schooling and children’s unobserved preferences for schooling. This is countered by a negative, structural relationship between parents’ and children’s schooling choices, resulting in an IV coefficient close to zero when exogenously increasing parents’ schooling. Nonetheless, an exogenous one-year increase in parents’ schooling increases children’s lifetime earnings by 1.2 percent on average.

None of the authors have received any funding for this research. We thank Antonio Ciccone, Mariacristina De Nardi, Steven Durlauf, Eric French and Chris Taber for very helpful conversations. The paper also benefited from conference and seminar participants at the 2015 ASSA and EEA meetings, University of Cyprus, Essex, and TSE. Junjie Guo and Wei Song provided outstanding research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

MARC RIS BibTeΧ

Download Citation Data

  • data appendix

More from NBER

In addition to working papers , the NBER disseminates affiliates’ latest findings through a range of free periodicals — the NBER Reporter , the NBER Digest , the Bulletin on Retirement and Disability , the Bulletin on Health , and the Bulletin on Entrepreneurship  — as well as online conference reports , video lectures , and interviews .

15th Annual Feldstein Lecture, Mario Draghi, "The Next Flight of the Bumblebee: The Path to Common Fiscal Policy in the Eurozone cover slide

effects of online classes essay

Alcohol Exclusion Laws and Its Drawbacks

Article sidebar.

effects of online classes essay

Main Article Content

Photo ID 143764115 © Chris Dorney | Dreamstime.com

INTRODUCTION

Since the repeal of the 18 th Amendment in 1933, alcohol consumption has become prevalent among many Americans. Alcohol intoxication is an increasing contributor to emergency room visits wherein individuals present to the emergency department (ED) in an inebriated state,  often with secondary injuries or severe medical co-morbidities related to alcohol poisoning. The ED is a stressful environment with providers working under taxing conditions while triaging difficult cases. Alcohol related visits contribute to this added stress for staff given that intoxicated individuals increase wait times for the ED, use up valuable resources, and have the capacity to act violently towards providers. As one nurse puts it, some intoxicated individuals  present with “an aggressive state, perhaps have been in a fight, blood everywhere, careening  around the place – it can make things very difficult.” [1] To combat these circumstances, thirty-four States including the District of Columbia have implemented a countermeasure recognized as Alcohol Exclusion Laws (AELs). 

AELs reduce or cut insurance coverage of certain visits to the ED if the cause of the visit is due to alcohol intoxication. [2] The vast implementation of this law is derived from the idea of individual decision making, that it is an individual’s choice to consume alcohol, and therefore they hold a personal responsibility for their intoxication. By using insurance coverage as a leverage, the law aims at reducing the number of ED visits relating to alcohol intoxication, saving resources, and deterring irresponsible drinking. While the intention behind AELs aims for positive change, it is unethical to use AELs, a form of financial leverage, to address certain problems within emergency medicine. 

Stigma is prominent in almost all substance abuse cases including those seen with alcohol intoxication. Many patients feel embarrassment or shame when seeking medical attention for a condition that was brought on by alcohol misuse. A personal account by Jonathan Hunt Glassman, a former alcoholic and NBC contributor, emphasizes on this negative bias. He knows firsthand how unsettling an ED visit can be. He felt demoralized from a superficial prognosis  made by a nurse on his complex alcohol abuse condition, in which the nurse said, “You need to  stop drinking.” [3]

Whether it be from shame or insecurities about an individual’s condition, the stigma behind substance abuse cases in the emergency department and the daunting task of asking for help can turn a lot of patients away from seeking and receiving medical treatment. The implementation of Alcohol Exclusion Laws can amplify this already present stigma. A study conducted by the National Institute of Health (NIH) analyzed States that implemented and continued to enforce Alcohol Exclusion Laws and the stigma in those states surrounding alcohol-related ED visits. The result from the study showed that AELs correlated with an increase in stigmatization regarding medical attention for alcohol-related incidents, and that AELs “negatively impact people’s willingness to seek medical care after alcohol-related injuries or  illnesses.” [4] Both the NIH study and the personal account by Hunt-Glassman go on to show that  AELs have the adverse effect of reinforcing the stigma surrounding alcohol cases in the ED.  While the idea behind AELs is in good faith, it contributes to the stigma. This contribution ethically challenges the idea that the emergency room is a space where the treatment of injuries is carried out without biases infringing on such medical care. The mission of EDs is to provide medical care to anyone in need. AELs have the effect of discouraging these patients from seeking help with the unintended consequence of doing them harm. 

A point of argument for the implementation of AELs is that it is the individual’s choice to be intoxicated and therefore justifiable that an individual receives less insurance coverage for medical expenses from a preventable intoxication. The idea of it being an individual choice to become intoxicated is one of the strongest supports for these exclusion laws. However, it is unjust to assume that all alcohol intoxications come by choice. Instances that disprove this assumption include both the college party scene and bar scene. Spiked drinks significantly increase alcohol concentration and can cause any responsible drinker to become intoxicated without intention or against their will. Additionally, alcoholic beverages served in various social gatherings like those in or around college campuses may not have a clear percentage of alcohol determination. Liquor containing high percentages of alcohol, such as Everclear which contains up to 190 proofs, are often masked by sweeteners and flavorings. Cocktails like these can cause a person to become dangerously intoxicated without their realization or intention. Some may argue that consuming an alcoholic beverage still holds accountability, that the person should be aware of the potential for a tampered drink, and therefore AELs should remain in use to deter this. However, like any law, AELs needs to have defined restrictions and/or exemptions. If the individual choice argument is used in favor for AELs, then how far reaching can the laws be applied? An attorney who specializes in these exclusion laws believes that AELs often offer more ambiguity than clarification when it comes to insurance policy, which leads to further ways insurance claims can be denied. [5]

In summary, the idea behind the use of Alcohol Exclusion Laws aims to reduce intoxication cases in the ED, however, there are drawbacks and aspects of this law that challenge the ethics of seeking medical care from the emergency department. The present stigma surrounding going to the ED for alcohol-related emergencies is already prevalent in hospitals across the country. When applying AELs, the present stigma may be magnified and further push the idea that seeking help for alcohol-related emergencies is shameful and embarrassing for patients, and therefore should be punished via financial means. Secondly, one of the main justifications for AELs is the idea that it is a deliberate intention to become intoxicated. It isn’t always the intention of individuals to get drunk when they choose to consume alcohol. There are additional factors that may play a part to exonerate a person’s accountability. It is difficult for people to recall the specifics of a situation when they become intoxicated; in some cases, accountability cannot be determined and the used of AELs can become unjustified. Overall, Alcohol Exclusion  Laws try to solve the issue of alcohol incidents in a way that produces more detriment than progress. A method to combat the issue of irresponsible drinking and intoxication in the emergency room within the US should not use AELs and financial leverage as one of its forefronts. In fact, a study that based its findings obtained from the Behavioral Risk Factor  Surveillance System nationwide survey that spanned twenty-four years from 1993-2017,  showed no real impact on binge drinking or increased alcohol consumption. [6] Given the downsides to AELs and its proven non-significant effects, several States have already repealed their AELs. For all these reasons, it would be beneficial to find an alternate method to address alcohol related issues within healthcare.

[1] Gregory, A. (16 Jun 2014). Nurses say drunk patients should be banned from A&E as ‘waste of resources’ UK:  Mirror. https://www.mirror.co.uk/news/uk-news/nurses-say-drunk-patients-should-3706280 2 (Jan 2008).

[2] Alcohol Exclusion Laws. National Highway Traffic Safety Administration. https://www.nhtsa.gov/sites/nhtsa.gov/files/810885.pdf.

[3] Glassman, J.H. (28 Apr 2022). Why don’t alcoholics get prescribed the medication they need?. NBC.  https://www.nbcnews.com/think/opinion/alcohol-related-deaths-er-visits-rose-covid-solution-use- rcna26425.

[4] Azagba, S., Ebling, T., Hall, M., (2023). Health claims denial for alcohol intoxication: State laws and structural stigma. Wiley Online Library . https://onlinelibrary.wiley.com/doi/10.1111/acer.15153. 

[5] (7 Sep 2021). The Alcohol Exclusion Chart Denied Life Insurance Claim. https://www.lifeinsuranceattorney.com/blog/2021/september/the-alcohol-exclusion-state-chart-denied-life-in/.

[6] Azagba, S., Shan, L., Ebling, T., Wolfson, M., Hall, M., Chaloupka, F., (26 Nov 2022). Does state repeal of alcohol  exclusion laws increase problem drinking? National Institutes of Health . https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10099925/.

William Ngo

Second place winner of Voices in Bioethics' 2023 persuasive essay contest. 

Disclaimer: These essays are submissions for the 2023 essay contest and have not undergone peer review or editing.

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License .

  • MHC's currently closed. MHC will open Mon at 9:00 AM
  • More Library Hours
  • Visit the Library
  • Today’s Hours: 8:00 am - 8:00 pm

Ask a Librarian

  • MHC Blog Home

Selections from the Papers of Murdina MacFarquhar Desmond, MD Digitized and Online

Murdina MacFarqurah Desmond working in a laboratory at George Washington University, 1947-1948

By Joy Oria, Archives Intern

Almost two hundred items from the Murdina MacFarquhar Desmond, MD papers have been digitized and are now available online. Dr. Desmond (1916-2003) contributed to the fields of pediatrics and neonatology during her long tenure (1948-1986) on the pediatrics faculty of Baylor College of Medicine and service to Jefferson Davis Hospital and Texas Children’s Hospital .

Among the digitized items are photographs of Jefferson Davis Hospital’s staff, nurseries, and infant patients. They demonstrate Dr. Desmond’s research for the Hartford Project in the 1960s to the closure of the hospital in 1989.

Photographs of speakers and attendees at events such as award ceremonies for Dr. Desmond and her retirement in 1986 document the neonatology and pediatrics communities of Houston and the United States. These images show figures such as Dr. Arnold J. Rudolph, Dr. L. Stanley James, and Dr. Charles W. Daeschner, as well as hospital leaders like Dr. Russell Blattner and Dr. Ralph Feigin from Baylor College of Medicine and Texas Children’s Hospital. Women in medicine are also well represented with photographs of Dr. Martha Yow, Dr. Katherine Hsu, Dr. Catherine Roett-Reid, Dr. Florence Heys, Dr. Reba Michels Hill, and Dr. Leora Andrew.

Given Dr. Desmond’s long affiliation with Baylor College of Medicine, much material represents Baylor’s history. Group faculty photographs of the pediatrics department range from a mere five people in 1954 to over eighty people in 1966. A panoramic black-and-white print shows the newly constructed Roy and Lillie Cullen Building as it appeared in 1947.

Dr. Desmond’s service in the U.S. Naval Reserve – Women’s Reserve during World War II is documented with photographs of her military service and uniform. Digital images of her insignia show the threadwork that created these historical badges for women serving in the Naval Reserve, also known as WAVES (Women Accepted for Volunteer Emergency Service).

Transcripts of oral history interviews and speeches can also be found in this collection. Dr. Desmond recalls her experiences entering the medical profession, handling epidemics in the nurseries of Jefferson Davis Hospital, and serving on the care team of David Vetter, a well-known patient of severe combined immunodeficiency.

A few historical postcards representing infant care in the early 1900s show early incubators and baby bottles.

If you’d like to learn more about Dr. Murdina MacFarquhar Desmond or other history-making women in the archives, contact us or make an appointment to visit the McGovern Historical Center.

[Dr. Paul Gillette and Dr. Murdina Desmond examine an infant in an incubator, circa 1970. MS054-b1f5-008, Murdina MacFarquhar Desmond, MD papers, McGovern Historical Center, Texas Medical Center Library]

The Texas Medical Center Library. 1133 John Freeman Blvd, Houston, TX 77030

More results...

A generative AI reset: Rewiring to turn potential into value in 2024

It’s time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI’s enormous potential value is harder than expected .

With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI transformations: competitive advantage comes from building organizational and technological capabilities to broadly innovate, deploy, and improve solutions at scale—in effect, rewiring the business  for distributed digital and AI innovation.

About QuantumBlack, AI by McKinsey

QuantumBlack, McKinsey’s AI arm, helps companies transform using the power of technology, technical expertise, and industry experts. With thousands of practitioners at QuantumBlack (data engineers, data scientists, product managers, designers, and software engineers) and McKinsey (industry and domain experts), we are working to solve the world’s most important AI challenges. QuantumBlack Labs is our center of technology development and client innovation, which has been driving cutting-edge advancements and developments in AI through locations across the globe.

Companies looking to score early wins with gen AI should move quickly. But those hoping that gen AI offers a shortcut past the tough—and necessary—organizational surgery are likely to meet with disappointing results. Launching pilots is (relatively) easy; getting pilots to scale and create meaningful value is hard because they require a broad set of changes to the way work actually gets done.

Let’s briefly look at what this has meant for one Pacific region telecommunications company. The company hired a chief data and AI officer with a mandate to “enable the organization to create value with data and AI.” The chief data and AI officer worked with the business to develop the strategic vision and implement the road map for the use cases. After a scan of domains (that is, customer journeys or functions) and use case opportunities across the enterprise, leadership prioritized the home-servicing/maintenance domain to pilot and then scale as part of a larger sequencing of initiatives. They targeted, in particular, the development of a gen AI tool to help dispatchers and service operators better predict the types of calls and parts needed when servicing homes.

Leadership put in place cross-functional product teams with shared objectives and incentives to build the gen AI tool. As part of an effort to upskill the entire enterprise to better work with data and gen AI tools, they also set up a data and AI academy, which the dispatchers and service operators enrolled in as part of their training. To provide the technology and data underpinnings for gen AI, the chief data and AI officer also selected a large language model (LLM) and cloud provider that could meet the needs of the domain as well as serve other parts of the enterprise. The chief data and AI officer also oversaw the implementation of a data architecture so that the clean and reliable data (including service histories and inventory databases) needed to build the gen AI tool could be delivered quickly and responsibly.

Our book Rewired: The McKinsey Guide to Outcompeting in the Age of Digital and AI (Wiley, June 2023) provides a detailed manual on the six capabilities needed to deliver the kind of broad change that harnesses digital and AI technology. In this article, we will explore how to extend each of those capabilities to implement a successful gen AI program at scale. While recognizing that these are still early days and that there is much more to learn, our experience has shown that breaking open the gen AI opportunity requires companies to rewire how they work in the following ways.

Figure out where gen AI copilots can give you a real competitive advantage

The broad excitement around gen AI and its relative ease of use has led to a burst of experimentation across organizations. Most of these initiatives, however, won’t generate a competitive advantage. One bank, for example, bought tens of thousands of GitHub Copilot licenses, but since it didn’t have a clear sense of how to work with the technology, progress was slow. Another unfocused effort we often see is when companies move to incorporate gen AI into their customer service capabilities. Customer service is a commodity capability, not part of the core business, for most companies. While gen AI might help with productivity in such cases, it won’t create a competitive advantage.

To create competitive advantage, companies should first understand the difference between being a “taker” (a user of available tools, often via APIs and subscription services), a “shaper” (an integrator of available models with proprietary data), and a “maker” (a builder of LLMs). For now, the maker approach is too expensive for most companies, so the sweet spot for businesses is implementing a taker model for productivity improvements while building shaper applications for competitive advantage.

Much of gen AI’s near-term value is closely tied to its ability to help people do their current jobs better. In this way, gen AI tools act as copilots that work side by side with an employee, creating an initial block of code that a developer can adapt, for example, or drafting a requisition order for a new part that a maintenance worker in the field can review and submit (see sidebar “Copilot examples across three generative AI archetypes”). This means companies should be focusing on where copilot technology can have the biggest impact on their priority programs.

Copilot examples across three generative AI archetypes

  • “Taker” copilots help real estate customers sift through property options and find the most promising one, write code for a developer, and summarize investor transcripts.
  • “Shaper” copilots provide recommendations to sales reps for upselling customers by connecting generative AI tools to customer relationship management systems, financial systems, and customer behavior histories; create virtual assistants to personalize treatments for patients; and recommend solutions for maintenance workers based on historical data.
  • “Maker” copilots are foundation models that lab scientists at pharmaceutical companies can use to find and test new and better drugs more quickly.

Some industrial companies, for example, have identified maintenance as a critical domain for their business. Reviewing maintenance reports and spending time with workers on the front lines can help determine where a gen AI copilot could make a big difference, such as in identifying issues with equipment failures quickly and early on. A gen AI copilot can also help identify root causes of truck breakdowns and recommend resolutions much more quickly than usual, as well as act as an ongoing source for best practices or standard operating procedures.

The challenge with copilots is figuring out how to generate revenue from increased productivity. In the case of customer service centers, for example, companies can stop recruiting new agents and use attrition to potentially achieve real financial gains. Defining the plans for how to generate revenue from the increased productivity up front, therefore, is crucial to capturing the value.

Upskill the talent you have but be clear about the gen-AI-specific skills you need

By now, most companies have a decent understanding of the technical gen AI skills they need, such as model fine-tuning, vector database administration, prompt engineering, and context engineering. In many cases, these are skills that you can train your existing workforce to develop. Those with existing AI and machine learning (ML) capabilities have a strong head start. Data engineers, for example, can learn multimodal processing and vector database management, MLOps (ML operations) engineers can extend their skills to LLMOps (LLM operations), and data scientists can develop prompt engineering, bias detection, and fine-tuning skills.

A sample of new generative AI skills needed

The following are examples of new skills needed for the successful deployment of generative AI tools:

  • data scientist:
  • prompt engineering
  • in-context learning
  • bias detection
  • pattern identification
  • reinforcement learning from human feedback
  • hyperparameter/large language model fine-tuning; transfer learning
  • data engineer:
  • data wrangling and data warehousing
  • data pipeline construction
  • multimodal processing
  • vector database management

The learning process can take two to three months to get to a decent level of competence because of the complexities in learning what various LLMs can and can’t do and how best to use them. The coders need to gain experience building software, testing, and validating answers, for example. It took one financial-services company three months to train its best data scientists to a high level of competence. While courses and documentation are available—many LLM providers have boot camps for developers—we have found that the most effective way to build capabilities at scale is through apprenticeship, training people to then train others, and building communities of practitioners. Rotating experts through teams to train others, scheduling regular sessions for people to share learnings, and hosting biweekly documentation review sessions are practices that have proven successful in building communities of practitioners (see sidebar “A sample of new generative AI skills needed”).

It’s important to bear in mind that successful gen AI skills are about more than coding proficiency. Our experience in developing our own gen AI platform, Lilli , showed us that the best gen AI technical talent has design skills to uncover where to focus solutions, contextual understanding to ensure the most relevant and high-quality answers are generated, collaboration skills to work well with knowledge experts (to test and validate answers and develop an appropriate curation approach), strong forensic skills to figure out causes of breakdowns (is the issue the data, the interpretation of the user’s intent, the quality of metadata on embeddings, or something else?), and anticipation skills to conceive of and plan for possible outcomes and to put the right kind of tracking into their code. A pure coder who doesn’t intrinsically have these skills may not be as useful a team member.

While current upskilling is largely based on a “learn on the job” approach, we see a rapid market emerging for people who have learned these skills over the past year. That skill growth is moving quickly. GitHub reported that developers were working on gen AI projects “in big numbers,” and that 65,000 public gen AI projects were created on its platform in 2023—a jump of almost 250 percent over the previous year. If your company is just starting its gen AI journey, you could consider hiring two or three senior engineers who have built a gen AI shaper product for their companies. This could greatly accelerate your efforts.

Form a centralized team to establish standards that enable responsible scaling

To ensure that all parts of the business can scale gen AI capabilities, centralizing competencies is a natural first move. The critical focus for this central team will be to develop and put in place protocols and standards to support scale, ensuring that teams can access models while also minimizing risk and containing costs. The team’s work could include, for example, procuring models and prescribing ways to access them, developing standards for data readiness, setting up approved prompt libraries, and allocating resources.

While developing Lilli, our team had its mind on scale when it created an open plug-in architecture and setting standards for how APIs should function and be built.  They developed standardized tooling and infrastructure where teams could securely experiment and access a GPT LLM , a gateway with preapproved APIs that teams could access, and a self-serve developer portal. Our goal is that this approach, over time, can help shift “Lilli as a product” (that a handful of teams use to build specific solutions) to “Lilli as a platform” (that teams across the enterprise can access to build other products).

For teams developing gen AI solutions, squad composition will be similar to AI teams but with data engineers and data scientists with gen AI experience and more contributors from risk management, compliance, and legal functions. The general idea of staffing squads with resources that are federated from the different expertise areas will not change, but the skill composition of a gen-AI-intensive squad will.

Set up the technology architecture to scale

Building a gen AI model is often relatively straightforward, but making it fully operational at scale is a different matter entirely. We’ve seen engineers build a basic chatbot in a week, but releasing a stable, accurate, and compliant version that scales can take four months. That’s why, our experience shows, the actual model costs may be less than 10 to 15 percent of the total costs of the solution.

Building for scale doesn’t mean building a new technology architecture. But it does mean focusing on a few core decisions that simplify and speed up processes without breaking the bank. Three such decisions stand out:

  • Focus on reusing your technology. Reusing code can increase the development speed of gen AI use cases by 30 to 50 percent. One good approach is simply creating a source for approved tools, code, and components. A financial-services company, for example, created a library of production-grade tools, which had been approved by both the security and legal teams, and made them available in a library for teams to use. More important is taking the time to identify and build those capabilities that are common across the most priority use cases. The same financial-services company, for example, identified three components that could be reused for more than 100 identified use cases. By building those first, they were able to generate a significant portion of the code base for all the identified use cases—essentially giving every application a big head start.
  • Focus the architecture on enabling efficient connections between gen AI models and internal systems. For gen AI models to work effectively in the shaper archetype, they need access to a business’s data and applications. Advances in integration and orchestration frameworks have significantly reduced the effort required to make those connections. But laying out what those integrations are and how to enable them is critical to ensure these models work efficiently and to avoid the complexity that creates technical debt  (the “tax” a company pays in terms of time and resources needed to redress existing technology issues). Chief information officers and chief technology officers can define reference architectures and integration standards for their organizations. Key elements should include a model hub, which contains trained and approved models that can be provisioned on demand; standard APIs that act as bridges connecting gen AI models to applications or data; and context management and caching, which speed up processing by providing models with relevant information from enterprise data sources.
  • Build up your testing and quality assurance capabilities. Our own experience building Lilli taught us to prioritize testing over development. Our team invested in not only developing testing protocols for each stage of development but also aligning the entire team so that, for example, it was clear who specifically needed to sign off on each stage of the process. This slowed down initial development but sped up the overall delivery pace and quality by cutting back on errors and the time needed to fix mistakes.

Ensure data quality and focus on unstructured data to fuel your models

The ability of a business to generate and scale value from gen AI models will depend on how well it takes advantage of its own data. As with technology, targeted upgrades to existing data architecture  are needed to maximize the future strategic benefits of gen AI:

  • Be targeted in ramping up your data quality and data augmentation efforts. While data quality has always been an important issue, the scale and scope of data that gen AI models can use—especially unstructured data—has made this issue much more consequential. For this reason, it’s critical to get the data foundations right, from clarifying decision rights to defining clear data processes to establishing taxonomies so models can access the data they need. The companies that do this well tie their data quality and augmentation efforts to the specific AI/gen AI application and use case—you don’t need this data foundation to extend to every corner of the enterprise. This could mean, for example, developing a new data repository for all equipment specifications and reported issues to better support maintenance copilot applications.
  • Understand what value is locked into your unstructured data. Most organizations have traditionally focused their data efforts on structured data (values that can be organized in tables, such as prices and features). But the real value from LLMs comes from their ability to work with unstructured data (for example, PowerPoint slides, videos, and text). Companies can map out which unstructured data sources are most valuable and establish metadata tagging standards so models can process the data and teams can find what they need (tagging is particularly important to help companies remove data from models as well, if necessary). Be creative in thinking about data opportunities. Some companies, for example, are interviewing senior employees as they retire and feeding that captured institutional knowledge into an LLM to help improve their copilot performance.
  • Optimize to lower costs at scale. There is often as much as a tenfold difference between what companies pay for data and what they could be paying if they optimized their data infrastructure and underlying costs. This issue often stems from companies scaling their proofs of concept without optimizing their data approach. Two costs generally stand out. One is storage costs arising from companies uploading terabytes of data into the cloud and wanting that data available 24/7. In practice, companies rarely need more than 10 percent of their data to have that level of availability, and accessing the rest over a 24- or 48-hour period is a much cheaper option. The other costs relate to computation with models that require on-call access to thousands of processors to run. This is especially the case when companies are building their own models (the maker archetype) but also when they are using pretrained models and running them with their own data and use cases (the shaper archetype). Companies could take a close look at how they can optimize computation costs on cloud platforms—for instance, putting some models in a queue to run when processors aren’t being used (such as when Americans go to bed and consumption of computing services like Netflix decreases) is a much cheaper option.

Build trust and reusability to drive adoption and scale

Because many people have concerns about gen AI, the bar on explaining how these tools work is much higher than for most solutions. People who use the tools want to know how they work, not just what they do. So it’s important to invest extra time and money to build trust by ensuring model accuracy and making it easy to check answers.

One insurance company, for example, created a gen AI tool to help manage claims. As part of the tool, it listed all the guardrails that had been put in place, and for each answer provided a link to the sentence or page of the relevant policy documents. The company also used an LLM to generate many variations of the same question to ensure answer consistency. These steps, among others, were critical to helping end users build trust in the tool.

Part of the training for maintenance teams using a gen AI tool should be to help them understand the limitations of models and how best to get the right answers. That includes teaching workers strategies to get to the best answer as fast as possible by starting with broad questions then narrowing them down. This provides the model with more context, and it also helps remove any bias of the people who might think they know the answer already. Having model interfaces that look and feel the same as existing tools also helps users feel less pressured to learn something new each time a new application is introduced.

Getting to scale means that businesses will need to stop building one-off solutions that are hard to use for other similar use cases. One global energy and materials company, for example, has established ease of reuse as a key requirement for all gen AI models, and has found in early iterations that 50 to 60 percent of its components can be reused. This means setting standards for developing gen AI assets (for example, prompts and context) that can be easily reused for other cases.

While many of the risk issues relating to gen AI are evolutions of discussions that were already brewing—for instance, data privacy, security, bias risk, job displacement, and intellectual property protection—gen AI has greatly expanded that risk landscape. Just 21 percent of companies reporting AI adoption say they have established policies governing employees’ use of gen AI technologies.

Similarly, a set of tests for AI/gen AI solutions should be established to demonstrate that data privacy, debiasing, and intellectual property protection are respected. Some organizations, in fact, are proposing to release models accompanied with documentation that details their performance characteristics. Documenting your decisions and rationales can be particularly helpful in conversations with regulators.

In some ways, this article is premature—so much is changing that we’ll likely have a profoundly different understanding of gen AI and its capabilities in a year’s time. But the core truths of finding value and driving change will still apply. How well companies have learned those lessons may largely determine how successful they’ll be in capturing that value.

Eric Lamarre

The authors wish to thank Michael Chui, Juan Couto, Ben Ellencweig, Josh Gartner, Bryce Hall, Holger Harreis, Phil Hudelson, Suzana Iacob, Sid Kamath, Neerav Kingsland, Kitti Lakner, Robert Levin, Matej Macak, Lapo Mori, Alex Peluffo, Aldo Rosales, Erik Roth, Abdul Wahab Shaikh, and Stephen Xu for their contributions to this article.

This article was edited by Barr Seitz, an editorial director in the New York office.

Explore a career with us

Related articles.

Light dots and lines evolve into a pattern of a human face and continue to stream off the the side in a moving grid pattern.

The economic potential of generative AI: The next productivity frontier

A yellow wire shaped into a butterfly

Rewired to outcompete

A digital construction of a human face consisting of blocks

Meet Lilli, our generative AI tool that’s a researcher, a time saver, and an inspiration

  • Share full article

Advertisement

Supported by

4 Ways a Settlement Could Change the Housing Industry

The influential National Association of Realtors agreed to make several changes to its policies to settle class-action lawsuits brought by home sellers who say they were forced to pay inflated commissions to real estate agents.

That National Association of Realtors building in Chicago.

By Debra Kamin

In the early hours of Friday morning, the National Association of Realtors agreed to a global settlement deal that would resolve several lawsuits against the trade group.

A group of Missouri home sellers sued N.A.R. over their policies on agent compensation, arguing that a N.A.R. rule requiring home sellers to pay commissions to their agents and the agents of their buyers led to inflated fees and price fixing. The lawsuit also called into a question another rule requiring agents to list homes on N.A.R.-affiliated databases in order to sell them. In October, a jury agreed that both practices were anticompetitive, and a judge ordered damages of at least $1.8 billion.

More than a dozen copycat cases, all accusing N.A.R. of stifling competition and violating antitrust laws, have followed.

With the settlement agreement, N.A.R. will pay $418 million in damages , but more important, it has agreed to rewrite a number of rules that have long been central to the U.S. housing industry. Here’s how things stand to change, pending court approval.

Home prices will drop.

In the United States, most agents specify a commission of 5 or 6 percent, paid by the seller. That means that someone with a $1 million home should expect to spend up to $60,000 on real estate commissions alone, with $30,000 going to his agent and $30,000 going to the agent who brings a buyer. Even for a home that costs $400,000 — close to the current median for homes across the United States — sellers are still paying around $24,000 in commissions, a cost that is baked into the final sales price of the home.

With the settlement agreement, sellers’ agents will no longer be required to make offers of commission to buyers’ agents, a practice called decoupling. This will save homeowners billions.

“Decoupling will allow commissions to be removed and negotiated down, lowering both housing prices and overall consumer costs,” said Steve Brobeck, the retired executive director of the Consumer Federation of America. Mr. Brobeck said that Americans spend about $100 billion a year in real estate commissions, and with the settlement, that number is expected to dip by at least $20 billion and up to $50 billion.

Since commissions are tacked onto the price of a home, “Over time, both sellers and buyers will force rates down through negotiation and comparison shopping in a more price-transparent marketplace,” he said.

The 6 percent commission will cease to be the norm.

The lawsuits argued that N.A.R., and brokerages that required their agents to be members of N.A.R., had set rules that led to an industrywide standard commission of 5 or 6 percent — one of the highest rates in the world. Without that guaranteed rate, agents will now most likely be forced to lower their commissions to compete for business.

“U.S. commissions are unlikely to decline to the 1 or 2 percent rate level in England, where only one agent and an attorney are usually involved in a home sale. But they certainly will decline substantially, and commissions will also increasingly reflect the competence and efforts of agents on sales,” Mr. Brobeck said in an email.

Steering — the practice of agents directing buyers to more expensive houses — will be less common.

Most of the databases where homes are listed for sale in the United States are restricted to dues-paying members who belong to N.A.R., a dominance that has led to antitrust allegations against N.A.R.

One N.A.R. rule demands that a listing agent, when posting a home on the database, clearly state the amount of compensation that a buying agent will receive should they bring a buyer. This is a practice that critics say has long led to “steering,” in which buyers’ agents direct their clients to pricier homes in a bid to collect a bigger commission check.

Under the settlement, any fields displaying broker compensation will be eliminated entirely, which will help damper the practice.

About one million real estate agents could leave the profession.

The number of real estate agents swelled during the pandemic, when mortgage rates plummeted and the housing market boomed. In 2020 and 2021, more than 156,000 people got their real estate licenses, and membership in the National Association of Realtors hit a peak of 1.6 million members in 2022.

A lot of that growth was predicated on the idea of easy money.

But now a lot of those agents are struggling, and a reduction in commission rates will only increase the pain. Half of the agents in the country sold one house — or no house s at all — last year. With the industry now staring down a massive overhaul, veteran agents predict their less experienced peers will leave the field all together.

Some analysts predict a mass departure. One widely cited report from investment banking firm Keefe, Bruyette & Woods projects 1 million agents leaving the field as shared commissions vanish.

“Veteran agents have built strong relationships, established reputations and extensive networks. Newer real estate agents may struggle,” said Jen McDonald, who leads LPT Realty in Reno, Nev., and has spent 24 years in the industry. “Without established reputations or strong clients bases, they are going to find it challenging to retain clients or attract new ones.”

Debra Kamin reports on real estate, covering what it means to buy, sell and own a home in America today. More about Debra Kamin

U.S. flag

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

  • Publications
  • Account settings
  • Advanced Search
  • Journal List
  • Springer Nature - PMC COVID-19 Collection

Logo of phenaturepg

Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19

1 Chitkara College of Hospitality Management, Chitkara University, Chandigarh, Punjab India

Varsha Singh

Arun aggarwal.

2 Chitkara Business School, Chitkara University, Chandigarh, Punjab India

The aim of the study is to identify the factors affecting students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19 and to establish the relationship between these variables. The study is quantitative in nature, and the data were collected from 544 respondents through online survey who were studying the business management (B.B.A or M.B.A) or hotel management courses in Indian universities. Structural equation modeling was used to analyze the proposed hypotheses. The results show that four independent factors used in the study viz. quality of instructor, course design, prompt feedback, and expectation of students positively impact students’ satisfaction and further student’s satisfaction positively impact students’ performance. For educational management, these four factors are essential to have a high level of satisfaction and performance for online courses. This study is being conducted during the epidemic period of COVID- 19 to check the effect of online teaching on students’ performance.

Introduction

Coronavirus is a group of viruses that is the main root of diseases like cough, cold, sneezing, fever, and some respiratory symptoms (WHO, 2019 ). Coronavirus is a contagious disease, which is spreading very fast amongst the human beings. COVID-19 is a new sprain which was originated in Wuhan, China, in December 2019. Coronavirus circulates in animals, but some of these viruses can transmit between animals and humans (Perlman & Mclntosh, 2020 ). As of March 282,020, according to the MoHFW, a total of 909 confirmed COVID-19 cases (862 Indians and 47 foreign nationals) had been reported in India (Centers for Disease Control and Prevention, 2020 ). Officially, no vaccine or medicine is evaluated to cure the spread of COVID-19 (Yu et al., 2020 ). The influence of the COVID-19 pandemic on the education system leads to schools and colleges’ widespread closures worldwide. On March 24, India declared a country-wide lockdown of schools and colleges (NDTV, 2020 ) for preventing the transmission of the coronavirus amongst the students (Bayham & Fenichel, 2020 ). School closures in response to the COVID-19 pandemic have shed light on several issues affecting access to education. COVID-19 is soaring due to which the huge number of children, adults, and youths cannot attend schools and colleges (UNESCO, 2020 ). Lah and Botelho ( 2012 ) contended that the effect of school closing on students’ performance is hazy.

Similarly, school closing may also affect students because of disruption of teacher and students’ networks, leading to poor performance. Bridge ( 2020 ) reported that schools and colleges are moving towards educational technologies for student learning to avoid a strain during the pandemic season. Hence, the present study’s objective is to develop and test a conceptual model of student’s satisfaction pertaining to online teaching during COVID-19, where both students and teachers have no other option than to use the online platform uninterrupted learning and teaching.

UNESCO recommends distance learning programs and open educational applications during school closure caused by COVID-19 so that schools and teachers use to teach their pupils and bound the interruption of education. Therefore, many institutes go for the online classes (Shehzadi et al., 2020 ).

As a versatile platform for learning and teaching processes, the E-learning framework has been increasingly used (Salloum & Shaalan, 2018 ). E-learning is defined as a new paradigm of online learning based on information technology (Moore et al., 2011 ). In contrast to traditional learning academics, educators, and other practitioners are eager to know how e-learning can produce better outcomes and academic achievements. Only by analyzing student satisfaction and their performance can the answer be sought.

Many comparative studies have been carried out to prove the point to explore whether face-to-face or traditional teaching methods are more productive or whether online or hybrid learning is better (Lockman & Schirmer, 2020 ; Pei & Wu, 2019 ; González-Gómez et al., 2016 ; González-Gómez et al., 2016 ). Results of the studies show that the students perform much better in online learning than in traditional learning. Henriksen et al. ( 2020 ) highlighted the problems faced by educators while shifting from offline to online mode of teaching. In the past, several research studies had been carried out on online learning to explore student satisfaction, acceptance of e-learning, distance learning success factors, and learning efficiency (Sher, 2009 ; Lee, 2014 ; Yen et al., 2018 ). However, scant amount of literature is available on the factors that affect the students’ satisfaction and performance in online classes during the pandemic of Covid-19 (Rajabalee & Santally, 2020 ). In the present study, the authors proposed that course design, quality of the instructor, prompt feedback, and students’ expectations are the four prominent determinants of learning outcome and satisfaction of the students during online classes (Lee, 2014 ).

The Course Design refers to curriculum knowledge, program organization, instructional goals, and course structure (Wright, 2003 ). If well planned, course design increasing the satisfaction of pupils with the system (Almaiah & Alyoussef, 2019 ). Mtebe and Raisamo ( 2014 ) proposed that effective course design will help in improving the performance through learners knowledge and skills (Khan & Yildiz, 2020 ; Mohammed et al., 2020 ). However, if the course is not designed effectively then it might lead to low usage of e-learning platforms by the teachers and students (Almaiah & Almulhem, 2018 ). On the other hand, if the course is designed effectively then it will lead to higher acceptance of e-learning system by the students and their performance also increases (Mtebe & Raisamo, 2014 ). Hence, to prepare these courses for online learning, many instructors who are teaching blended courses for the first time are likely to require a complete overhaul of their courses (Bersin, 2004 ; Ho et al., 2006 ).

The second-factor, Instructor Quality, plays an essential role in affecting the students’ satisfaction in online classes. Instructor quality refers to a professional who understands the students’ educational needs, has unique teaching skills, and understands how to meet the students’ learning needs (Luekens et al., 2004 ). Marsh ( 1987 ) developed five instruments for measuring the instructor’s quality, in which the main method was Students’ Evaluation of Educational Quality (SEEQ), which delineated the instructor’s quality. SEEQ is considered one of the methods most commonly used and embraced unanimously (Grammatikopoulos et al., 2014 ). SEEQ was a very useful method of feedback by students to measure the instructor’s quality (Marsh, 1987 ).

The third factor that improves the student’s satisfaction level is prompt feedback (Kinicki et al., 2004 ). Feedback is defined as information given by lecturers and tutors about the performance of students. Within this context, feedback is a “consequence of performance” (Hattie & Timperley, 2007 , p. 81). In education, “prompt feedback can be described as knowing what you know and what you do not related to learning” (Simsek et al., 2017 , p.334). Christensen ( 2014 ) studied linking feedback to performance and introduced the positivity ratio concept, which is a mechanism that plays an important role in finding out the performance through feedback. It has been found that prompt feedback helps in developing a strong linkage between faculty and students which ultimately leads to better learning outcomes (Simsek et al., 2017 ; Chang, 2011 ).

The fourth factor is students’ expectation . Appleton-Knapp and Krentler ( 2006 ) measured the impact of student’s expectations on their performance. They pin pointed that the student expectation is important. When the expectations of the students are achieved then it lead to the higher satisfaction level of the student (Bates & Kaye, 2014 ). These findings were backed by previous research model “Student Satisfaction Index Model” (Zhang et al., 2008 ). However, when the expectations are students is not fulfilled then it might lead to lower leaning and satisfaction with the course. Student satisfaction is defined as students’ ability to compare the desired benefit with the observed effect of a particular product or service (Budur et al., 2019 ). Students’ whose grade expectation is high will show high satisfaction instead of those facing lower grade expectations.

The scrutiny of the literature show that although different researchers have examined the factors affecting student satisfaction but none of the study has examined the effect of course design, quality of the instructor, prompt feedback, and students’ expectations on students’ satisfaction with online classes during the pandemic period of Covid-19. Therefore, this study tries to explore the factors that affect students’ satisfaction and performance regarding online classes during the pandemic period of COVID–19. As the pandemic compelled educational institutions to move online with which they were not acquainted, including teachers and learners. The students were not mentally prepared for such a shift. Therefore, this research will be examined to understand what factors affect students and how students perceived these changes which are reflected through their satisfaction level.

This paper is structured as follows: The second section provides a description of theoretical framework and the linkage among different research variables and accordingly different research hypotheses were framed. The third section deals with the research methodology of the paper as per APA guideline. The outcomes and corresponding results of the empirical analysis are then discussed. Lastly, the paper concludes with a discussion and proposes implications for future studies.

Theoretical framework

Achievement goal theory (AGT) is commonly used to understand the student’s performance, and it is proposed by four scholars Carole Ames, Carol Dweck, Martin Maehr, and John Nicholls in the late 1970s (Elliot, 2005 ). Elliott & Dweck ( 1988 , p11) define that “an achievement goal involves a program of cognitive processes that have cognitive, affective and behavioral consequence”. This theory suggests that students’ motivation and achievement-related behaviors can be easily understood by the purpose and the reasons they adopted while they are engaged in the learning activities (Dweck & Leggett, 1988 ; Ames, 1992 ; Urdan, 1997 ). Some of the studies believe that there are four approaches to achieve a goal, i.e., mastery-approach, mastery avoidance, performance approach, and performance-avoidance (Pintrich, 1999 ; Elliot & McGregor, 2001 ; Schwinger & Stiensmeier-Pelster, 2011 , Hansen & Ringdal, 2018 ; Mouratidis et al., 2018 ). The environment also affects the performance of students (Ames & Archer, 1988 ). Traditionally, classroom teaching is an effective method to achieve the goal (Ames & Archer, 1988 ; Ames, 1992 ; Clayton et al., 2010 ) however in the modern era, the internet-based teaching is also one of the effective tools to deliver lectures, and web-based applications are becoming modern classrooms (Azlan et al., 2020 ). Hence, following section discuss about the relationship between different independent variables and dependent variables (Fig. ​ (Fig.1 1 ).

An external file that holds a picture, illustration, etc.
Object name is 10639_2021_10523_Fig1_HTML.jpg

Proposed Model

Hypotheses development

Quality of the instructor and satisfaction of the students.

Quality of instructor with high fanaticism on student’s learning has a positive impact on their satisfaction. Quality of instructor is one of the most critical measures for student satisfaction, leading to the education process’s outcome (Munteanu et al., 2010 ; Arambewela & Hall, 2009 ; Ramsden, 1991 ). Suppose the teacher delivers the course effectively and influence the students to do better in their studies. In that case, this process leads to student satisfaction and enhances the learning process (Ladyshewsky, 2013 ). Furthermore, understanding the need of learner by the instructor also ensures student satisfaction (Kauffman, 2015 ). Hence the hypothesis that the quality of instructor significantly affects the satisfaction of the students was included in this study.

  • H1: The quality of the instructor positively affects the satisfaction of the students.

Course design and satisfaction of students

The course’s technological design is highly persuading the students’ learning and satisfaction through their course expectations (Liaw, 2008 ; Lin et al., 2008 ). Active course design indicates the students’ effective outcomes compared to the traditional design (Black & Kassaye, 2014 ). Learning style is essential for effective course design (Wooldridge, 1995 ). While creating an online course design, it is essential to keep in mind that we generate an experience for students with different learning styles. Similarly, (Jenkins, 2015 ) highlighted that the course design attributes could be developed and employed to enhance student success. Hence the hypothesis that the course design significantly affects students’ satisfaction was included in this study.

  • H2: Course design positively affects the satisfaction of students.

Prompt feedback and satisfaction of students

The emphasis in this study is to understand the influence of prompt feedback on satisfaction. Feedback gives the information about the students’ effective performance (Chang, 2011 ; Grebennikov & Shah, 2013 ; Simsek et al., 2017 ). Prompt feedback enhances student learning experience (Brownlee et al., 2009 ) and boosts satisfaction (O'donovan, 2017 ). Prompt feedback is the self-evaluation tool for the students (Rogers, 1992 ) by which they can improve their performance. Eraut ( 2006 ) highlighted the impact of feedback on future practice and student learning development. Good feedback practice is beneficial for student learning and teachers to improve students’ learning experience (Yorke, 2003 ). Hence the hypothesis that prompt feedback significantly affects satisfaction was included in this study.

  • H3: Prompt feedback of the students positively affects the satisfaction.

Expectations and satisfaction of students

Expectation is a crucial factor that directly influences the satisfaction of the student. Expectation Disconfirmation Theory (EDT) (Oliver, 1980 ) was utilized to determine the level of satisfaction based on their expectations (Schwarz & Zhu, 2015 ). Student’s expectation is the best way to improve their satisfaction (Brown et al., 2014 ). It is possible to recognize student expectations to progress satisfaction level (ICSB, 2015 ). Finally, the positive approach used in many online learning classes has been shown to place a high expectation on learners (Gold, 2011 ) and has led to successful outcomes. Hence the hypothesis that expectations of the student significantly affect the satisfaction was included in this study.

  • H4: Expectations of the students positively affects the satisfaction.

Satisfaction and performance of the students

Zeithaml ( 1988 ) describes that satisfaction is the outcome result of the performance of any educational institute. According to Kotler and Clarke ( 1986 ), satisfaction is the desired outcome of any aim that amuses any individual’s admiration. Quality interactions between instructor and students lead to student satisfaction (Malik et al., 2010 ; Martínez-Argüelles et al., 2016 ). Teaching quality and course material enhances the student satisfaction by successful outcomes (Sanderson, 1995 ). Satisfaction relates to the student performance in terms of motivation, learning, assurance, and retention (Biner et al., 1996 ). Mensink and King ( 2020 ) described that performance is the conclusion of student-teacher efforts, and it shows the interest of students in the studies. The critical element in education is students’ academic performance (Rono, 2013 ). Therefore, it is considered as center pole, and the entire education system rotates around the student’s performance. Narad and Abdullah ( 2016 ) concluded that the students’ academic performance determines academic institutions’ success and failure.

Singh et al. ( 2016 ) asserted that the student academic performance directly influences the country’s socio-economic development. Farooq et al. ( 2011 ) highlights the students’ academic performance is the primary concern of all faculties. Additionally, the main foundation of knowledge gaining and improvement of skills is student’s academic performance. According to Narad and Abdullah ( 2016 ), regular evaluation or examinations is essential over a specific period of time in assessing students’ academic performance for better outcomes. Hence the hypothesis that satisfaction significantly affects the performance of the students was included in this study.

  • H5: Students’ satisfaction positively affects the performance of the students.

Satisfaction as mediator

Sibanda et al. ( 2015 ) applied the goal theory to examine the factors persuading students’ academic performance that enlightens students’ significance connected to their satisfaction and academic achievement. According to this theory, students perform well if they know about factors that impact on their performance. Regarding the above variables, institutional factors that influence student satisfaction through performance include course design and quality of the instructor (DeBourgh, 2003 ; Lado et al., 2003 ), prompt feedback, and expectation (Fredericksen et al., 2000 ). Hence the hypothesis that quality of the instructor, course design, prompts feedback, and student expectations significantly affect the students’ performance through satisfaction was included in this study.

  • H6: Quality of the instructor, course design, prompt feedback, and student’ expectations affect the students’ performance through satisfaction.
  • H6a: Students’ satisfaction mediates the relationship between quality of the instructor and student’s performance.
  • H6b: Students’ satisfaction mediates the relationship between course design and student’s performance.
  • H6c: Students’ satisfaction mediates the relationship between prompt feedback and student’s performance.
  • H6d: Students’ satisfaction mediates the relationship between student’ expectations and student’s performance.

Participants

In this cross-sectional study, the data were collected from 544 respondents who were studying the management (B.B.A or M.B.A) and hotel management courses. The purposive sampling technique was used to collect the data. Descriptive statistics shows that 48.35% of the respondents were either MBA or BBA and rests of the respondents were hotel management students. The percentages of male students were (71%) and female students were (29%). The percentage of male students is almost double in comparison to females. The ages of the students varied from 18 to 35. The dominant group was those aged from 18 to 22, and which was the under graduation student group and their ratio was (94%), and another set of students were from the post-graduation course, which was (6%) only.

The research instrument consists of two sections. The first section is related to demographical variables such as discipline, gender, age group, and education level (under-graduate or post-graduate). The second section measures the six factors viz. instructor’s quality, course design, prompt feedback, student expectations, satisfaction, and performance. These attributes were taken from previous studies (Yin & Wang, 2015 ; Bangert, 2004 ; Chickering & Gamson, 1987 ; Wilson et al., 1997 ). The “instructor quality” was measured through the scale developed by Bangert ( 2004 ). The scale consists of seven items. The “course design” and “prompt feedback” items were adapted from the research work of Bangert ( 2004 ). The “course design” scale consists of six items. The “prompt feedback” scale consists of five items. The “students’ expectation” scale consists of five items. Four items were adapted from Bangert, 2004 and one item was taken from Wilson et al. ( 1997 ). Students’ satisfaction was measure with six items taken from Bangert ( 2004 ); Wilson et al. ( 1997 ); Yin and Wang ( 2015 ). The “students’ performance” was measured through the scale developed by Wilson et al. ( 1997 ). The scale consists of six items. These variables were accessed on a five-point likert scale, ranging from 1(strongly disagree) to 5(strongly agree). Only the students from India have taken part in the survey. A total of thirty-four questions were asked in the study to check the effect of the first four variables on students’ satisfaction and performance. For full details of the questionnaire, kindly refer Appendix Tables ​ Tables6 6 .

The study used a descriptive research design. The factors “instructor quality, course design, prompt feedback and students’ expectation” were independent variables. The students’ satisfaction was mediator and students’ performance was the dependent variable in the current study.

In this cross-sectional research the respondents were selected through judgment sampling. They were informed about the objective of the study and information gathering process. They were assured about the confidentiality of the data and no incentive was given to then for participating in this study. The information utilizes for this study was gathered through an online survey. The questionnaire was built through Google forms, and then it was circulated through the mails. Students’ were also asked to write the name of their college, and fifteen colleges across India have taken part to fill the data. The data were collected in the pandemic period of COVID-19 during the total lockdown in India. This was the best time to collect the data related to the current research topic because all the colleges across India were involved in online classes. Therefore, students have enough time to understand the instrument and respondent to the questionnaire in an effective manner. A total of 615 questionnaires were circulated, out of which the students returned 574. Thirty responses were not included due to the unengaged responses. Finally, 544 questionnaires were utilized in the present investigation. Male and female students both have taken part to fill the survey, different age groups, and various courses, i.e., under graduation and post-graduation students of management and hotel management students were the part of the sample.

Exploratory factor analysis (EFA)

To analyze the data, SPSS and AMOS software were used. First, to extract the distinct factors, an exploratory factor analysis (EFA) was performed using VARIMAX rotation on a sample of 544. Results of the exploratory analysis rendered six distinct factors. Factor one was named as the quality of instructor, and some of the items were “The instructor communicated effectively”, “The instructor was enthusiastic about online teaching” and “The instructor was concerned about student learning” etc. Factor two was labeled as course design, and the items were “The course was well organized”, “The course was designed to allow assignments to be completed across different learning environments.” and “The instructor facilitated the course effectively” etc. Factor three was labeled as prompt feedback of students, and some of the items were “The instructor responded promptly to my questions about the use of Webinar”, “The instructor responded promptly to my questions about general course requirements” etc. The fourth factor was Student’s Expectations, and the items were “The instructor provided models that clearly communicated expectations for weekly group assignments”, “The instructor used good examples to explain statistical concepts” etc. The fifth factor was students’ satisfaction, and the items were “The online classes were valuable”, “Overall, I am satisfied with the quality of this course” etc. The sixth factor was performance of the student, and the items were “The online classes has sharpened my analytic skills”, “Online classes really tries to get the best out of all its students” etc. These six factors explained 67.784% of the total variance. To validate the factors extracted through EFA, the researcher performed confirmatory factor analysis (CFA) through AMOS. Finally, structural equation modeling (SEM) was used to test the hypothesized relationships.

Measurement model

The results of Table ​ Table1 1 summarize the findings of EFA and CFA. Results of the table showed that EFA renders six distinct factors, and CFA validated these factors. Table ​ Table2 2 shows that the proposed measurement model achieved good convergent validity (Aggarwal et al., 2018a , b ). Results of the confirmatory factor analysis showed that the values of standardized factor loadings were statistically significant at the 0.05 level. Further, the results of the measurement model also showed acceptable model fit indices such that CMIN = 710.709; df = 480; CMIN/df = 1.481 p  < .000; Incremental Fit Index (IFI) = 0.979; Tucker-Lewis Index (TLI) = 0.976; Goodness of Fit index (GFI) = 0.928; Adjusted Goodness of Fit Index (AGFI) = 0.916; Comparative Fit Index (CFI) = 0.978; Root Mean Square Residual (RMR) = 0.042; Root Mean Squared Error of Approximation (RMSEA) = 0.030 is satisfactory.

Factor Analysis

Author’s Compilation

Validity analysis of measurement model

Author’s compilation

AVE is the Average Variance Extracted, CR is Composite Reliability

The bold diagonal value represents the square root of AVE

The Average Variance Explained (AVE) according to the acceptable index should be higher than the value of squared correlations between the latent variables and all other variables. The discriminant validity is confirmed (Table ​ (Table2) 2 ) as the value of AVE’s square root is greater than the inter-construct correlations coefficient (Hair et al., 2006 ). Additionally, the discriminant validity existed when there was a low correlation between each variable measurement indicator with all other variables except with the one with which it must be theoretically associated (Aggarwal et al., 2018a , b ; Aggarwal et al., 2020 ). The results of Table ​ Table2 2 show that the measurement model achieved good discriminate validity.

Structural model

To test the proposed hypothesis, the researcher used the structural equation modeling technique. This is a multivariate statistical analysis technique, and it includes the amalgamation of factor analysis and multiple regression analysis. It is used to analyze the structural relationship between measured variables and latent constructs.

Table  3 represents the structural model’s model fitness indices where all variables put together when CMIN/DF is 2.479, and all the model fit values are within the particular range. That means the model has attained a good model fit. Furthermore, other fit indices as GFI = .982 and AGFI = 0.956 be all so supportive (Schumacker & Lomax, 1996 ; Marsh & Grayson, 1995 ; Kline, 2005 ).

Criterion for model fit

Hence, the model fitted the data successfully. All co-variances among the variables and regression weights were statistically significant ( p  < 0.001).

Table ​ Table4 4 represents the relationship between exogenous, mediator and endogenous variables viz—quality of instructor, prompt feedback, course design, students’ expectation, students’ satisfaction and students’ performance. The first four factors have a positive relationship with satisfaction, which further leads to students’ performance positively. Results show that the instructor’s quality has a positive relationship with the satisfaction of students for online classes (SE = 0.706, t-value = 24.196; p  < 0.05). Hence, H1 was supported. The second factor is course design, which has a positive relationship with students’ satisfaction of students (SE = 0.064, t-value = 2.395; p < 0.05). Hence, H2 was supported. The third factor is Prompt feedback, and results show that feedback has a positive relationship with the satisfaction of the students (SE = 0.067, t-value = 2.520; p < 0.05). Hence, H3 was supported. The fourth factor is students’ expectations. The results show a positive relationship between students’ expectation and students’ satisfaction with online classes (SE = 0.149, t-value = 5.127; p < 0.05). Hence, H4 was supported. The results of SEM show that out of quality of instructor, prompt feedback, course design, and students’ expectation, the most influencing factor that affect the students’ satisfaction was instructor’s quality (SE = 0.706) followed by students’ expectation (SE =5.127), prompt feedback (SE = 2.520). The factor that least affects the students’ satisfaction was course design (2.395). The results of Table ​ Table4 4 finally depicts that students’ satisfaction has positive effect on students’ performance ((SE = 0.186, t-value = 2.800; p < 0.05). Hence H5 was supported.

Structural analysis

Table ​ Table5 5 shows that students’ satisfaction partially mediates the positive relationship between the instructor’s quality and student performance. Hence, H6(a) was supported. Further, the mediation analysis results showed that satisfaction again partially mediates the positive relationship between course design and student’s performance. Hence, H6(b) was supported However, the mediation analysis results showed that satisfaction fully mediates the positive relationship between prompt feedback and student performance. Hence, H6(c) was supported. Finally, the results of the Table ​ Table5 5 showed that satisfaction partially mediates the positive relationship between expectations of the students and student’s performance. Hence, H6(d) was supported.

Mediation Analysis

In the present study, the authors evaluated the different factors directly linked with students’ satisfaction and performance with online classes during Covid-19. Due to the pandemic situation globally, all the colleges and universities were shifted to online mode by their respective governments. No one has the information that how long this pandemic will remain, and hence the teaching method was shifted to online mode. Even though some of the educators were not tech-savvy, they updated themselves to battle the unexpected circumstance (Pillai et al., 2021 ). The present study results will help the educators increase the student’s satisfaction and performance in online classes. The current research assists educators in understanding the different factors that are required for online teaching.

Comparing the current research with past studies, the past studies have examined the factors affecting the student’s satisfaction in the conventional schooling framework. However, the present study was conducted during India’s lockdown period to identify the prominent factors that derive the student’s satisfaction with online classes. The study also explored the direct linkage between student’s satisfaction and their performance. The present study’s findings indicated that instructor’s quality is the most prominent factor that affects the student’s satisfaction during online classes. This means that the instructor needs to be very efficient during the lectures. He needs to understand students’ psychology to deliver the course content prominently. If the teacher can deliver the course content properly, it affects the student’s satisfaction and performance. The teachers’ perspective is critical because their enthusiasm leads to a better online learning process quality.

The present study highlighted that the second most prominent factor affecting students’ satisfaction during online classes is the student’s expectations. Students might have some expectations during the classes. If the instructor understands that expectation and customizes his/her course design following the student’s expectations, then it is expected that the students will perform better in the examinations. The third factor that affects the student’s satisfaction is feedback. After delivering the course, appropriate feedback should be taken by the instructors to plan future courses. It also helps to make the future strategies (Tawafak et al., 2019 ). There must be a proper feedback system for improvement because feedback is the course content’s real image. The last factor that affects the student’s satisfaction is design. The course content needs to be designed in an effective manner so that students should easily understand it. If the instructor plans the course, so the students understand the content without any problems it effectively leads to satisfaction, and the student can perform better in the exams. In some situations, the course content is difficult to deliver in online teaching like the practical part i.e. recipes of dishes or practical demonstration in the lab. In such a situation, the instructor needs to be more creative in designing and delivering the course content so that it positively impacts the students’ overall satisfaction with online classes.

Overall, the students agreed that online teaching was valuable for them even though the online mode of classes was the first experience during the pandemic period of Covid-19 (Agarwal & Kaushik, 2020 ; Rajabalee & Santally, 2020 ). Some of the previous studies suggest that the technology-supported courses have a positive relationship with students’ performance (Cho & Schelzer, 2000 ; Harasim, 2000 ; Sigala, 2002 ). On the other hand, the demographic characteristic also plays a vital role in understanding the online course performance. According to APA Work Group of the Board of Educational Affairs ( 1997 ), the learner-centered principles suggest that students must be willing to invest the time required to complete individual course assignments. Online instructors must be enthusiastic about developing genuine instructional resources that actively connect learners and encourage them toward proficient performances. For better performance in studies, both teachers and students have equal responsibility. When the learner faces any problem to understand the concepts, he needs to make inquiries for the instructor’s solutions (Bangert, 2004 ). Thus, we can conclude that “instructor quality, student’s expectation, prompt feedback, and effective course design” significantly impact students’ online learning process.

Implications of the study

The results of this study have numerous significant practical implications for educators, students and researchers. It also contributes to the literature by demonstrating that multiple factors are responsible for student satisfaction and performance in the context of online classes during the period of the COVID-19 pandemic. This study was different from the previous studies (Baber, 2020 ; Ikhsan et al., 2019 ; Eom & Ashill, 2016 ). None of the studies had examined the effect of students’ satisfaction on their perceived academic performance. The previous empirical findings have highlighted the importance of examining the factors affecting student satisfaction (Maqableh & Jaradat, 2021 ; Yunusa & Umar, 2021 ). Still, none of the studies has examined the effect of course design, quality of instructor, prompt feedback, and students’ expectations on students’ satisfaction all together with online classes during the pandemic period. The present study tries to fill this research gap.

The first essential contribution of this study was the instructor’s facilitating role, and the competence he/she possesses affects the level of satisfaction of the students (Gray & DiLoreto, 2016 ). There was an extra obligation for instructors who taught online courses during the pandemic. They would have to adapt to a changing climate, polish their technical skills throughout the process, and foster new students’ technical knowledge in this environment. The present study’s findings indicate that instructor quality is a significant determinant of student satisfaction during online classes amid a pandemic. In higher education, the teacher’s standard referred to the instructor’s specific individual characteristics before entering the class (Darling-Hammond, 2010 ). These attributes include factors such as instructor content knowledge, pedagogical knowledge, inclination, and experience. More significantly, at that level, the amount of understanding could be given by those who have a significant amount of technical expertise in the areas they are teaching (Martin, 2021 ). Secondly, the present study results contribute to the profession of education by illustrating a realistic approach that can be used to recognize students’ expectations in their class effectively. The primary expectation of most students before joining a university is employment. Instructors have agreed that they should do more to fulfill students’ employment expectations (Gorgodze et al., 2020 ). The instructor can then use that to balance expectations to improve student satisfaction. Study results can be used to continually improve and build courses, as well as to make policy decisions to improve education programs. Thirdly, from result outcomes, online course design and instructors will delve deeper into how to structure online courses more efficiently, including design features that minimize adversely and maximize optimistic emotion, contributing to greater student satisfaction (Martin et al., 2018 ). The findings suggest that the course design has a substantial positive influence on the online class’s student performance. The findings indicate that the course design of online classes need to provide essential details like course content, educational goals, course structure, and course output in a consistent manner so that students would find the e-learning system beneficial for them; this situation will enable students to use the system and that leads to student performance (Almaiah & Alyoussef, 2019 ). Lastly, the results indicate that instructors respond to questions promptly and provide timely feedback on assignments to facilitate techniques that help students in online courses improve instructor participation, instructor interaction, understanding, and participation (Martin et al., 2018 ). Feedback can be beneficial for students to focus on the performance that enhances their learning.

Limitations and future scope of the study

The data collected in this study was cross-sectional in nature due to which it is difficult to establish the causal relationship between the variables. The future research can use a longitudinal study to handle this limitation. Further, the data was collected from one type of respondents only, that is, the students. Therefore, the results of the study cannot be generalized to other samples. The future research can also include the perspectives of teachers and policy makers to have more generalization of the results. The current research is only limited to theory classes; therefore, it can be implemented to check students’ performance in practical classes. The study is done on the Indian students only; thus, if the data is collected from various countries, it can give better comparative results to understand the student’s perspective. This study is limited to check the performance of students, so in the future, the performance of teachers can be checked with similar kinds of conditions. There may be some issues and problems faced by the students, like the limited access to the internet or disturbance due to low signals. Some of the students may face the home environment issues such as disturbance due to family members, which may lead to negative performance. The above-mentioned points can be inculcated in the future research.

Declarations

Not applicable.

The authors declare no conflict of interest, financial or otherwise.

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Contributor Information

Ram Gopal, Email: [email protected] .

Varsha Singh, Email: [email protected] .

Arun Aggarwal, Email: [email protected] .

  • Agarwal S, Kaushik JS. Student’s perception of online learning during COVID pandemic. The Indian Journal of Pediatrics. 2020; 87 :554–554. doi: 10.1007/s12098-020-03327-7. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Aggarwal A, Dhaliwal RS, Nobi K. Impact of structural empowerment on organizational commitment: The mediating role of women's psychological empowerment. Vision. 2018; 22 (3):284–294. doi: 10.1177/0972262918786049. [ CrossRef ] [ Google Scholar ]
  • Aggarwal A, Goyal J, Nobi K. Examining the impact of leader-member exchange on perceptions of organizational justice: The mediating role of perceptions of organizational politics. Theoretical Economics Letters. 2018; 8 (11):2308–2329. doi: 10.4236/tel.2018.811150. [ CrossRef ] [ Google Scholar ]
  • Aggarwal A, Chand PA, Jhamb D, Mittal A. Leader-member exchange, work engagement and psychological withdrawal behaviour: The mediating role of psychological empowerment. Frontiers in Psychology. 2020; 11 :1–17. doi: 10.3389/fpsyg.2020.00423. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Almaiah MA, Almulhem A. A conceptual framework for determining the success factors of e-learning system implementation using Delphi technique. Journal of Theoretical and Applied Information Technology. 2018; 96 (17):5962–5976. [ Google Scholar ]
  • Almaiah MA, Alyoussef IY. Analysis of the effect of course design, course content support, course assessment and instructor characteristics on the actual use of E-learning system. Ieee Access. 2019; 7 :171907–171922. doi: 10.1109/ACCESS.2019.2956349. [ CrossRef ] [ Google Scholar ]
  • Ames C. Classrooms: Goals, structures, and student motivation. Journal of Educational Psychology. 1992; 84 :261–271. doi: 10.1037/0022-0663.84.3.261. [ CrossRef ] [ Google Scholar ]
  • Ames C, Archer J. Achievement goals in the classroom: Student's learning strategies and motivational processes. Journal of Educational Psychology. 1988; 80 :260–267. doi: 10.1037/0022-0663.80.3.260. [ CrossRef ] [ Google Scholar ]
  • APA Work Group of the Board of Educational Affairs . Learner-centered psychological principles: A framework for school reform and redesign. American Psychological Association; 1997. [ Google Scholar ]
  • Appleton-Knapp S, Krentler KA. Measuring student expectations and their effects on satisfaction: The importance of managing student expectations. Journal of Marketing Education. 2006; 28 (3):254–264. doi: 10.1177/0273475306293359. [ CrossRef ] [ Google Scholar ]
  • Arambewela R, Hall J. An empirical model of international student satisfaction. Asia Pacific Journal of Marketing and Logistics. 2009; 21 (4):555–569. doi: 10.1108/13555850910997599. [ CrossRef ] [ Google Scholar ]
  • Azlan AA, Hamzah MR, Sern TJ, Ayub SH, Mohamad E. Public knowledge, attitudes and practices towards COVID-19: A cross-sectional study in Malaysia. PLoS One. 2020; 15 (5):e0233668. doi: 10.1371/journal.pone.0233668. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Baber H. Determinants of Students' perceived outcome and satisfaction in online learning during the pandemic of COVID-19. Journal of Education and e-Learning Research. 2020; 7 (3):285–292. doi: 10.20448/journal.509.2020.73.285.292. [ CrossRef ] [ Google Scholar ]
  • Bangert AW. The seven principles of good practice: A framework for evaluating on- line teaching. The Internet and Higher Education. 2004; 7 (3):217–232. doi: 10.1016/j.iheduc.2004.06.003. [ CrossRef ] [ Google Scholar ]
  • Bates EA, Kaye LK. “I’d be expecting caviar in lectures”: The impact of the new fee regime on undergraduate students’ expectations of higher education. Higher Education. 2014; 67 (5):655–673. doi: 10.1007/s10734-013-9671-3. [ CrossRef ] [ Google Scholar ]
  • Bayham, J., & Fenichel, E.P. (2020). The impact of school closure for COVID-19 on the US healthcare workforce and the net mortality effects. Available at SSRN: 10.2139/ssrn.3555259.
  • Bersin J. The blended learning book: Best practices, proven methodologies and lessons learned. Pfeiffer Publishing; 2004. [ Google Scholar ]
  • Biner PM, Summers M, Dean RS, Bink ML, Anderson JL, Gelder BC. Student satisfaction with interactive telecourses as a function of demographic variables and prior telecourse experience. Distance Education. 1996; 17 (11):33–43. doi: 10.1080/0158791960170104. [ CrossRef ] [ Google Scholar ]
  • Black GS, Kassaye WW. Do students learning styles impact student outcomes in marketing classes? Academy of Educational Leadership Journal. 2014; 18 (4):149–162. [ Google Scholar ]
  • Bridge, S. (2020). Opinion: How edtech will keep our students on track during covid-19. Arabian business. Com Retrieved from https://search.proquest.com/docview/2377556452?accountid=147490 . Accessed 12 Oct 2020.
  • Brown SA, Venkatesh V, Goyal S. Expectation confirmation in information systems research: A test of six competing models. MIS Quarterly. 2014; 38 (3):729–756. doi: 10.25300/MISQ/2014/38.3.05. [ CrossRef ] [ Google Scholar ]
  • Brownlee J, Walker S, Lennox S, Exley B, Pearce S. The first year university experience: Using personal epistemology to understsnd effective learning and teaching in higher education. Higher Education. 2009; 58 (5):599–618. doi: 10.1007/s10734-009-9212-2. [ CrossRef ] [ Google Scholar ]
  • Budur T, Faraj KM, Karim LA. Benchmarking operations strategies via hybrid model: A case study of café-restaurant sector. Amozonia Investiga. 2019; 8 :842–854. [ Google Scholar ]
  • Centers for Disease Control and Prevention (2020). Coronavirus disease 2019 (COVID-19): Reducing stigma. Retrieved November 26, 2020, from: https://www.cdc.gov/coronavirus/2019-ncov/about/related-stigma.html .
  • Chang N. Pre-service Teachers' views: How did E-feedback through assessment facilitate their learning? Journal of the Scholarship of Teaching and Learning. 2011; 11 (2):16–33. [ Google Scholar ]
  • Chickering AW, Gamson ZF. Seven principles for good practice in undergraduate education. AAHE Bulletin. 1987; 39 (7):3–7. [ Google Scholar ]
  • Cho W, Schelzer C. Just in-time education: Tools for hospitality managers of the future? International Journal of Contemporary Hospitality Management. 2000; 12 (1):31–36. doi: 10.1108/09596110010305000. [ CrossRef ] [ Google Scholar ]
  • Christensen AL. Feedback, affect, and creative behavior: A multi-level model linking feedback to performance. Arizona State University; 2014. [ Google Scholar ]
  • Clayton K, Blumberg F, Auld DP. The relationship between motivation, learning strategies and choice of environment whether traditional or including an online component. British Journal of Educational Technology. 2010; 41 (3):349–364. doi: 10.1111/j.1467-8535.2009.00993.x. [ CrossRef ] [ Google Scholar ]
  • Darling-Hammond, L. (2010).  Evaluating teacher effectiveness: How teacher performance assessments can measure and improve teaching . Washington, DC: Center for American Progress
  • DeBourgh GA. Predictors of student satisfaction in distance-delivered graduate nursing courses: What matters most? Journal of Professional Nursing. 2003; 19 :149–163. doi: 10.1016/S8755-7223(03)00072-3. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Dweck C, Leggett E. A social–cognitive approach to motivation and personality. Psychological Review. 1988; 95 :256–273. doi: 10.1037/0033-295X.95.2.256. [ CrossRef ] [ Google Scholar ]
  • Elliot AJ. A conceptual history of the achievement goal construct. In: Elliot A, Dweck C, editors. Handbook of competence and motivation. Guilford Press; 2005. pp. 52–72. [ Google Scholar ]
  • Elliot A, McGregor H. A 2 _ 2 achievement goal framework. Journal of Personality and Social Psychology. 2001; 80 :501–519. doi: 10.1037/0022-3514.80.3.501. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Elliott ES, Dweck CS. Goals: An approach to motivation and achievement. Journal of Personality and Social Psychology. 1988; 54 (1):5. doi: 10.1037/0022-3514.54.1.5. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Eom SB, Ashill N. The determinants of students' perceived learning outcomes and satisfaction in university online education: An update. Decision Sciences Journal of Innovative Education. 2016; 14 (2):185–215. doi: 10.1111/dsji.12097. [ CrossRef ] [ Google Scholar ]
  • Eraut, M., (2006). Feedback. Learning in Health and Social Care. Volume-5, issue-3. Pg 111–118. Retrieved from https://edservices.wiley.com/how-student-feedback-creates-better-online- learning/ . Accessed 23 Oct 2020.
  • Farooq MS, Chaudhry AH, Shafiq M, Berhanu G. Factors affecting students' quality of academic performance: A case of secondary school level. Journal of Quality and Technology Management. 2011; 7 :1–14. [ Google Scholar ]
  • Fredericksen E, Shea P, Pickett A. Factors influencing student and faculty satisfaction in the SUNY learning network. State University of New York; 2000. [ Google Scholar ]
  • Gold, S. (2011). A constructivist approach to online training for online teachers. Journal of Aysnchronous Learning Networks, 5 (1), 35–57.
  • González-Gómez D, Jeong JS, Rodríguez DA. Performance and perception in the flipped learning model: An initial approach to evaluate the effectiveness of a new teaching methodology in a general science classroom. Journal of Science Education and Technology. 2016; 25 (3):450–459. doi: 10.1007/s10956-016-9605-9. [ CrossRef ] [ Google Scholar ]
  • Gorgodze S, Macharashvili L, Kamladze A. Learning for earning: Student expectations and perceptions of university. International Education Studies. 2020; 13 (1):42–53. doi: 10.5539/ies.v13n1p42. [ CrossRef ] [ Google Scholar ]
  • Grammatikopoulos, V., Linardakis, M., Gregoriadis, A., & Oikonomidis, V. (2014). Assessing the Students' evaluations of educational quality (SEEQ) questionnaire in Greek higher education. Higher Education., 70 (3).
  • Gray JA, DiLoreto M. The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation. 2016; 11 (1):n1. [ Google Scholar ]
  • Grebennikov L, Shah S. Monitoring trends in student satisfaction. Tertiary Education and Management. 2013; 19 (4):301–322. doi: 10.1080/13583883.2013.804114. [ CrossRef ] [ Google Scholar ]
  • Hair JF, Black WC, Babin BJ, Anderson RE, Tatham RL. Multivariate data analysis 6th edition. Pearson Prentice Hall. New Jersey. Humans: Critique and reformulation. Journal of Abnormal Psychology. 2006; 87 :49–74. [ PubMed ] [ Google Scholar ]
  • Hansen G, Ringdal R. Formative assessment as a future step in maintaining the mastery-approach and performance-avoidance goal stability. Studies in Educational Evaluation. 2018; 56 :59–70. doi: 10.1016/j.stueduc.2017.11.005. [ CrossRef ] [ Google Scholar ]
  • Harasim L. Shift happens: Online education as a new paradigm in learning. The Internet and Higher Education. 2000; 3 (1):41–61. doi: 10.1016/S1096-7516(00)00032-4. [ CrossRef ] [ Google Scholar ]
  • Hattie J, Timperley H. The power of feedback. Review of Educational Research. 2007; 77 (1):81–112. doi: 10.3102/003465430298487. [ CrossRef ] [ Google Scholar ]
  • Henriksen D, Creely E, Henderson M. Folk pedagogies for teacher transitions: Approaches to synchronous online learning in the wake of COVID-19. Journal of Technology and Teacher Education. 2020; 28 (2):201–209. [ Google Scholar ]
  • Ho A, Lu L, Thurmaier K. Testing the reluctant Professor's hypothesis: Evaluating a blended-learning approach to distance education. Journal of Public Affairs Education. 2006; 12 (1):81–102. doi: 10.1080/15236803.2006.12001414. [ CrossRef ] [ Google Scholar ]
  • ICSB (2015). Addressing undergraduate entrepreneurship student expectations: An exploratory study. International Council for Small Business (ICSB). Retrieved from https://search.proquest.com/docview/1826918813?accountid=147490 . Accessed 20 Oct 2020.
  • Ikhsan, R. B., Saraswati, L. A., Muchardie, B. G., & Susilo, A. (2019). The determinants of students' perceived learning outcomes and satisfaction in BINUS online learning. Paper presented at the 2019 5th International Conference on New Media Studies (CONMEDIA). IEEE.
  • Jenkins, D. M. (2015). Integrated course design: A facelift for college courses. Journal of Management Education, 39 (3), 427–432.
  • Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23 .
  • Khan NUS, Yildiz Y. Impact of intangible characteristics of universities on student satisfaction. Amazonia Investiga. 2020; 9 (26):105–116. doi: 10.34069/AI/2020.26.02.12. [ CrossRef ] [ Google Scholar ]
  • Kinicki AJ, Prussia GE, Wu BJ, McKee-Ryan FM. A covariance structure analysis of employees' response to performance feedback. Journal of Applied Psychology. 2004; 89 (6):1057–1069. doi: 10.1037/0021-9010.89.6.1057. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Kline RB. Principles and practice of structural equation modeling. 2. The Guilford Press; 2005. [ Google Scholar ]
  • Kotler, P., & Clarke, R. N. (1986). Marketing for health care organizations . Prentice Hall.
  • Lado N, Cardone-Riportella C, Rivera-Torres P. Measurement and effects of teaching quality: An empirical model applied to masters programs. Journal of Business Education. 2003; 4 :28–40. [ Google Scholar ]
  • Ladyshewsky RK. Instructor presence in online courses and student satisfaction. International Journal for the Scholarship of Teaching and Learning. 2013; 7 :1. doi: 10.20429/ijsotl.2013.070113. [ CrossRef ] [ Google Scholar ]
  • Lah, K., & G. Botelho. (2012). Union Opts to Continue Chicago Teachers Strike; Mayor Takes Fight to Court. http://articles.cnn.com/2012-09-16/us/us_illinois-chicago-teachersstrike_1_chicago-teachers-union-union-president-karen-lewis-union-delegates .
  • Lee J. An exploratory study of effective online learning: Assessing satisfaction levels of graduate students of mathematics education associated with human and design factors of an online course. The International Review of Research in Open and Distance Learning. 2014; 15 (1):111–132. doi: 10.19173/irrodl.v15i1.1638. [ CrossRef ] [ Google Scholar ]
  • Liaw S-S. Investigating students' perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the blackboard system. Computers & Education. 2008; 51 (2):864–873. doi: 10.1016/j.compedu.2007.09.005. [ CrossRef ] [ Google Scholar ]
  • Lin Y, Lin G, Laffey JM. Building a social and motivational framework for understanding satisfaction in online learning. Journal of Educational Computing Research. 2008; 38 (1):1–27. doi: 10.2190/EC.38.1.a. [ CrossRef ] [ Google Scholar ]
  • Lockman AS, Schirmer BR. Online instruction in higher education: Promising, research-based, and evidence-based practices. Journal of Education and e-Learning Research. 2020; 7 (2):130–152. doi: 10.20448/journal.509.2020.72.130.152. [ CrossRef ] [ Google Scholar ]
  • Luekens, M.T., Lyter, D.M., and Fox, E.E. (2004). Teacher attrition and mobility: Results from the teacher follow-up survey, 2000–01 (NCES 2004–301). National Center for Education Statistics, U.S. Department of Education . Washington, DC. https://nces.ed.gov/pubsearch/pubsinfo.asp?pubid=2004301 .
  • Malik, M. E., Danish, R. Q., & Usman, A. (2010). The impact of service quality on students’ satisfaction in higher education institutes of Punjab. Journal of Management Research, 2 (2), 1–11.
  • Maqableh, M., & Jaradat, M. (2021). Exploring the determinants of students’ academic performance at university level: The mediating role of internet usage continuance intention. Education and Information Technologies . 10.1007/s10639-021-10453-y. [ PMC free article ] [ PubMed ]
  • Marsh HW. Students' evaluations of university teaching: Research findings, methodological issues, and directions for future research. International Journal of Educational Research. 1987; 11 :253–388. doi: 10.1016/0883-0355(87)90001-2. [ CrossRef ] [ Google Scholar ]
  • Marsh, H. W., & Grayson, D. (1995). Latent variable models of multitrait-multimethod data.Marsh, H. W., & Grayson, D. (1995). Latent variable models of multitrait-multimethod data. In R. H. Hoyle (Ed.), Structural equation modeling: Concepts, issues, and applications (p. 177–198). Sage Publications, Inc.
  • Martin, A. M. (2021). Instructor qualities and student success in higher education online courses. Journal of Digital Learning in Teacher Education, 37 (1), 65–80.
  • Martínez-Argüelles, M. J., & Batalla-Busquets, J. M. (2016). Perceived service quality and student loyalty in an online university. International Review of Research in Open and Distributed Learning, 17 (4), 264–279.
  • Martin F, Wang C, Sadaf A. Student perception of helpfulness of facilitation strategies that enhance instructor presence, connectedness, engagement, and learning in online courses. The Internet and Higher Education. 2018; 37 :52–65. doi: 10.1016/j.iheduc.2018.01.003. [ CrossRef ] [ Google Scholar ]
  • Mensink PJ, King K. Student access of online feedback is modified by the availability of assessment marks, gender and academic performance. British Journal of Educational Technology. 2020; 51 (1):10–22. doi: 10.1111/bjet.12752. [ CrossRef ] [ Google Scholar ]
  • Mohammed SS, Suleyman C, Taylan B. Burnout determinants and consequences among university lecturers. Amazonia Investiga. 2020; 9 (27):13–24. doi: 10.34069/AI/2020.27.03.2. [ CrossRef ] [ Google Scholar ]
  • Moore JL, Dickson-Deane C, Galyen K. E-learning, online learning, and distance learning environments: Are they the same? Internet Higher Educ. 2011; 14 (2):129–135. doi: 10.1016/j.iheduc.2010.10.001. [ CrossRef ] [ Google Scholar ]
  • Mouratidis, A., Michou, A., Demircioğlu, A. N., & Sayil, M. (2018). Different goals, different pathways to success: Performance-approach goals as direct and mastery-approach goals as indirect predictors of grades in mathematics. Learning and Individual Differences, 61 , 127–135.
  • Mtebe JS, Raisamo R. A model for assessing learning management system success in higher education in sub-Saharan countries. The Electronic Journal of Information Systems in Developing Countries. 2014; 61 (1):1–17. doi: 10.1002/j.1681-4835.2014.tb00436.x. [ CrossRef ] [ Google Scholar ]
  • Munteanu C, Ceobanu C, Bobâlca C, Anton O. An analysis of customer satisfaction in a higher education context. The International Journal of Public Sector Management. 2010; 23 (2):124. doi: 10.1108/09513551011022483. [ CrossRef ] [ Google Scholar ]
  • Narad A, Abdullah B. Academic performance of senior secondary school students: Influence of parental encouragement and school environment. Rupkatha Journal on Interdisciplinary Studies in Humanities. 2016; 8 (2):12–19. doi: 10.21659/rupkatha.v8n2.02. [ CrossRef ] [ Google Scholar ]
  • NDTV (2020). Schools Closed, Travel To Be Avoided, Says Centre On Coronavirus: 10 Points. NDTV.com. Retrieved March 18, 2020.
  • O'donovan B. How student beliefs about knowledge and knowing influence their satisfaction with assessment and feedback. Higher Education. 2017; 74 (4):617–633. doi: 10.1007/s10734-016-0068-y. [ CrossRef ] [ Google Scholar ]
  • Oliver RL. A congitive model of the antecedents and consequences of satisfaction decisions. JMR, Journal of Marketing Research (Pre-1986) 1980; 17 (000004):460. doi: 10.1177/002224378001700405. [ CrossRef ] [ Google Scholar ]
  • Pei L, Wu H. Does online learning work better than offline learning in undergraduate medical education? A systematic review and meta-analysis. Medical Education Online. 2019; 24 (1):1666538. doi: 10.1080/10872981.2019.1666538. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Perlman S, & Mclntosh K. (2020). Coronaviruses, including severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS). In: J.E Benett, R. Dolin,  M. J. Blaser (Eds.), Mandell, Douglas, and Bennett's principles and practice of infectious diseases. 9th ed . Philadelphia, PA: Elsevier: chap 155.
  • Pillai, K. R., Upadhyaya, P., Prakash, A. V., Ramaprasad, B. S., Mukesh, H. V., & Pai, Y. (2021). End-user satisfaction of technology-enabled assessment in higher education: A coping theory perspective. Education and Information Technologies . 10.1007/s10639-020-10401-2.
  • Pintrich P. The role of motivation in promoting and sustaining self-regulated learning. International Journal of Educational Research. 1999; 31 :459–470. doi: 10.1016/S0883-0355(99)00015-4. [ CrossRef ] [ Google Scholar ]
  • Rajabalee, Y. B., & Santally, M. I. (2020). Learner satisfaction, engagement and performances in an online module: Implications for institutional e-learning policy. Education and Information Technologies . 10.1007/s10639-020-10375-1. [ PMC free article ] [ PubMed ]
  • Ramsden PA. Performance indicator of teaching quality in higher education: The course experience questionnaire. Studies in Higher Education. 1991; 16 (2):129–150. doi: 10.1080/03075079112331382944. [ CrossRef ] [ Google Scholar ]
  • Rogers J. Adults learning. 3. Open University Press; 1992. [ Google Scholar ]
  • Rono, R. (2013). Factors affecting pupils' performance in public primary schools at Kenya certificate of primary education examination (Kcpe) in Emgwen Division, Nandi District, Kenya (Doctoral dissertation, University of Nairobi) .
  • Salloum, S. A. and Shaalan, K. (2018). Investigating students' acceptance of e-learning system in higher educational environments in the UAE: Applying the extended technology acceptance model (TAM), Ph.D. dissertation, Brit. Univ. Dubai, Dubai, United Arab Emirates, 2018.
  • Sanderson G. Objectives and evaluation. In: Truelove S, editor. Handbook of training and development. 2. Blackwell; 1995. pp. 113–144. [ Google Scholar ]
  • Schumacker RE, Lomax RG. A beginner's guide to structural equation modeling. L. L. Erlbaum Associates; 1996. [ Google Scholar ]
  • Schwarz C, Zhu Z. The impact of student expectations in using instructional tools on student engagement: A look through the expectation disconfirmation theory lens. Journal of Information Systems Education. 2015; 26 (1):47–58. [ Google Scholar ]
  • Schwinger M, Stiensmeier-Pelster J. Performance-approach and performance-avoidance classroom goals and the adoption of personal achievement goals. British Journal of Educational Psychology. 2011; 81 (4):680–699. doi: 10.1111/j.2044-8279.2010.02012.x. [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Sher, A. (2009). Assessing the relationship of student-instructor and student-student interaction to student learning and satisfaction in web-based online learning environment. Journal of Interactive Online Learning, 8 (2).
  • Sibanda L, Iwu CG, Benedict OH. Factors influencing academic performance оf university students. Демографія та соціальна економіка 2015; 2 :103–115. [ Google Scholar ]
  • Sigala M. The evolution of internet pedagogy: Benefits for tourism and hospitality education. Journal of Hospitality, Leisure Sport and Tourism Education. 2002; 1 (2):29–45. [ Google Scholar ]
  • Simsek, U., Turan, I., & Simsek, U. (2017). Social studies teachers‟ and teacher candidates” perceptions on prompt feedback and communicate high expectations. PEOPLE: International Journal of Social Sciences, 3 (1), 332, 345.
  • Singh, S. P., Malik, S., & Singh, P. (2016). Factors affecting academic performance of students. Paripex-Indian Journal of Research, 5 (4), 176–178.
  • Shehzadi, S., Nisar, Q. A., Hussain, M. S., Basheer, M. F., Hameed, W. U., & Chaudhry, N. I. (2020). The role of digital learning toward students' satisfaction and university brand image at educational institutes of Pakistan: a post-effect of COVID-19. Asian Education and Development Studies, 10 (2), 276–294.
  • Tawafak RM, Romli AB, Alsinani M. E-learning system of UCOM for improving student assessment feedback in Oman higher education. Education and Information Technologies. 2019; 24 (2):1311–1335. doi: 10.1007/s10639-018-9833-0. [ CrossRef ] [ Google Scholar ]
  • UNESCO (2020). United nations educational, scientific and cultural organization. COVID19 educational disruption and response. UNESCO, Paris, France.  https://en.unesco.org/themes/education-emergencies/coronavirus-school-closures . Accessed 17 Nov 2020.
  • Urdan, T. (1997). Achievement goal theory: Past results, future directions. Advances in Motivation and Achievement, 10 , 99–141.
  • Wilson KL, Lizzio A, Ramsden P. The development, validation and application of the course experience questionnaire. Studies in Higher Education. 1997; 22 (1):33–53. doi: 10.1080/03075079712331381121. [ CrossRef ] [ Google Scholar ]
  • Wooldridge, B. (1995). Increasing the effectiveness of university/college instruction: Integrating the results of learning style research into course design and delivery. In R. R. Simms and S. J. Simms (Eds.), the Importance of Learning Styles. Westport, CT: Greenwood Press, 49–67.
  • World Health Organization (2019). https://www.who.int/health-topics/coronavirus#tab=tab_1 , Retrieved 29 March 2020.
  • Wright CR. Criteria for evaluating the quality of online courses. Alberta distance Educ. Training Assoc. 2003; 16 (2):185–200. [ Google Scholar ]
  • Yen SC, Lo Y, Lee A, Enriquez J. Learning online, offline, and in-between: Comparing student academic outcomes and course satisfaction in face-to-face, online, and blended teaching modalities. Education and Information Technologies. 2018; 23 (5):2141–2153. doi: 10.1007/s10639-018-9707-5. [ CrossRef ] [ Google Scholar ]
  • Yin H, Wang W. Assessing and improving the quality of undergraduate teaching in China: The course experience questionnaire. Assessment & Evaluation in Higher Education. 2015; 40 (8):1032–1049. doi: 10.1080/02602938.2014.963837. [ CrossRef ] [ Google Scholar ]
  • Yorke M. Formative assessment in higher education: Moves towards theory and the enhancement of pedagogic practice. Higher Education. 2003; 45 (4):477–501. doi: 10.1023/A:1023967026413. [ CrossRef ] [ Google Scholar ]
  • Yu F, Du L, Ojcius DM, Pan C, Jiang S. Measures for diagnosing and treating infections by a novel coronavirus responsible for a pneumonia outbreak originating in Wuhan, China. Microbes and Infection. 2020; 22 (2):74–79. doi: 10.1016/j.micinf.2020.01.003. [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
  • Yunusa AA, Umar IN. A scoping review of critical predictive factors (CPFs) of satisfaction and perceived learning outcomes in E-learning environments. Education and Information Technologies. 2021; 26 (1):1223–1270. doi: 10.1007/s10639-020-10286-1. [ CrossRef ] [ Google Scholar ]
  • Zeithaml VA. Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing. 1988; 52 (3):2–22. doi: 10.1177/002224298805200302. [ CrossRef ] [ Google Scholar ]
  • Zhang L, Han Z, Gao Q. Empirical study on the student satisfaction index in higher education. International Journal of Business and Management. 2008; 3 (9):46–51. [ Google Scholar ]

IMAGES

  1. Impact Of Online Education On Students || Essential Essay Writing

    effects of online classes essay

  2. Advantages And Disadvantages Of Online Learning

    effects of online classes essay

  3. Essay on Online Education

    effects of online classes essay

  4. Write a short essay on Effect of online Education

    effects of online classes essay

  5. Essay on Online Education/Classes Advantages & Disadvantages |Online

    effects of online classes essay

  6. 10 lines on online classes //essay on online classes 10 lines

    effects of online classes essay

VIDEO

  1. Helping students with essays I The best essay 2023

  2. Online classes v/s Physical classes || Traditional classes v/s Online classes || Online vs offline

  3. Important Essay Topics

  4. 8 Tips for writing an excellent essay

  5. Short Paragraph On My Experience Of Online Classes During Lockdown

  6. Essay on Need of Education for all // Essay Writing // English Essay// Content Writer ✍️

COMMENTS

  1. Impact of Online Classes on Students Essay

    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.

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

    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 ...

  3. Academic and emotional effects of online learning during the COVID-19

    Online learning during the COVID-19 pandemic has posed various challenges and opportunities for students and educators. This article reviews the academic and emotional effects of online learning on college students, and provides recommendations for improving their learning outcomes and well-being. The article also discusses the implications of online learning for future education and research.

  4. Essay On Online Education: In 100 Words, 150 Words, and 200 Words

    Essay on Online Education in 100 words. Online education is a modern educational paradigm where students access instructional content through the internet. This innovative approach has gained immense popularity, especially after the pandemic, owing to its convenience and adaptability. It has enabled students of all ages to acquire knowledge ...

  5. How Effective Is Online Learning? What the Research ...

    The use of virtual courses among K-12 students has grown rapidly in recent years. Florida, for example, requires all high school students to take at least one online course.

  6. Is Online Learning Effective?

    Now a report from UNESCO, the United Nations' educational and cultural organization, says that overreliance on remote learning technology during the pandemic led to "staggering" education ...

  7. Online education in the post-COVID era

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

  8. How does virtual learning impact students in higher education?

    The most compelling studies of online education draw on a random assignment design (i.e., randomized control trial or RCT) to isolate the causal effect of online versus in-person learning.

  9. Full article: Online Education: Worldwide Status, Challenges, Trends

    Online education is on track to become mainstream by 2025. ... Citation 2001) and over the years there has been increasing interest in online business education research. This essay is both timely and significant for several reasons. First, it focuses on the analysis of online business education. ... This will also avoid harmful effects of ...

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

    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.

  11. Effects of online education on mental and physical health

    Online Education surprisingly has resulted in a lack of Vitamin D. Lack of sunlight, poor diet, and exercise have resulted in more problems than one could anticipate. 6. Calcium deficiency. As weird as it sounds, the lack of physical activity and calcium has resulted in trivial injuries, thus resulting in extensive injuries further.

  12. Online and face‐to‐face learning: Evidence from students' performance

    The role of ICT in modulating the effect of education and lifelong learning on income inequality and economic growth in Africa. African Development Review, 31 (3), 261-274. [Google Scholar] Xu, D. , & Jaggars, S. S. (2014). Performance gaps between online and face‐to‐face courses: Differences across types of students and academic subject ...

  13. Capturing the benefits of remote learning

    In a recent study, researchers found that 18% of parents pointed to greater flexibility in a child's schedule or way of learning as the biggest benefit or positive outcome related to remote learning ( School Psychology, Roy, A., et al., in press).

  14. Online schooling: Who is harmed and who is helped?

    A pair of recent studies has examined the effect of online education among middle- and high-school students. The pattern of effects in these papers echoes that of the postsecondary findings. Both ...

  15. The pros and cons of online learning

    Pros of Online Learning. First, let's take a look at the true value of online learning by examining some of the benefits: 1. Flexibility. Online learning's most significant advantage is its flexibility. It's the reason millions of adults have chosen to continue their education and pursue certificates and degrees.

  16. The Impact of Online Learning on Student's Academic Performance

    online classes could affect the academic performance of students. This paper seeks to study the. impact of online learning on the academic performance of university students and to determine. whether education systems should increase the amount of online learning for traditional in-class. subjects.

  17. The Negative Effects of Online Learning

    The long-term effects of online learning are unknown, though the poll showed a variety of short-term repercussions. For example, online learning may be causing problems for college students' social lives and grades. In fact, the overall sentiment of, and expectations about the college experience, seem to be plummeting.

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

    Essay on Online Classes in 150 Words. Online classes have become a prevalent mode of education, especially in the past two years. These digital platforms offer several advantages. First, they provide flexibility, allowing students to learn from the comfort of their homes. This is especially beneficial for those with busy schedules or who are ...

  19. Online Classes Essay

    In this online classes essay in English, we will understand how the pandemic has affected the educational sector and online learning has benefited children. ... there are also positive and negative effects of online classes. But, the advantages outweigh limited interactions, and this is why online classes have a huge impact in this period. By ...

  20. Students' experience of online learning during the COVID‐19 pandemic: A

    Even in higher education, around 76% of students reported having incompatible devices for online learning and only 15% of students used laptop for online learning, whereas around 85% of them used smartphone (Agung et al., 2020). It is very likely that K‐12 students also suffer from this availability issue as they depend on their parents to ...

  21. Online Classes Vs. Traditional Classes Essay

    The article compares and contrasts online classes and traditional classes. Among the advantages of online classes are flexibility and convenience, while in-person classes offer a more structured learning environment. The author highlights that online lessons can be more cost-effective, although they lack support provided by live interactions.

  22. The Causal Effect of Parents' Education on Children's Earnings

    In addition to working papers, the NBER disseminates affiliates' latest findings through a range of free periodicals — the NBER Reporter, the NBER Digest, the Bulletin on Retirement and Disability, the Bulletin on Health, and the Bulletin on Entrepreneurship — as well as online conference reports, video lectures, and interviews.

  23. Alcohol Exclusion Laws and Its Drawbacks

    Voices in Bioethics is currently seeking submissions on philosophical and practical topics, both current and timeless. Papers addressing access to healthcare, the bioethical implications of recent Supreme Court rulings, environmental ethics, data privacy, cybersecurity, law and bioethics, economics and bioethics, reproductive ethics, research ethics, and pediatric bioethics are sought.

  24. Selections from the Papers of Murdina MacFarquhar Desmond, MD Digitized

    By Joy Oria, Archives Intern. Almost two hundred items from the Murdina MacFarquhar Desmond, MD papers have been digitized and are now available online. Dr. Desmond (1916-2003) contributed to the fields of pediatrics and neonatology during her long tenure (1948-1986) on the pediatrics faculty of Baylor College of Medicine and service to Jefferson Davis Hospital and Texas Children's Hospital.

  25. Students' online learning challenges during the pandemic and how they

    2. I fail to get appropriate help during online classes. 2.04: 1.44: 3. I lack the ability to control my own thoughts, emotions, and actions during online classes. 2.51: 1.65: 4. I have limited preparation before an online class. 2.68: 1.54: 5. I have poor time management skills during online classes. 2.50: 1.53: 6.

  26. A generative AI reset: Rewiring to turn potential into value in 2024

    It's time for a generative AI (gen AI) reset. The initial enthusiasm and flurry of activity in 2023 is giving way to second thoughts and recalibrations as companies realize that capturing gen AI's enormous potential value is harder than expected.. With 2024 shaping up to be the year for gen AI to prove its value, companies should keep in mind the hard lessons learned with digital and AI ...

  27. 4 Ways a Settlement Could Change the Housing Industry

    The influential National Association of Realtors agreed to make several changes to its policies to settle class-action lawsuits brought by home sellers who say they were forced to pay inflated ...

  28. Impact of online classes on the satisfaction and performance of

    The online classes has sharpened my analytic skills: 3.08: 0.82: 0.815: An online class really tries to get the best out of all its students: 3.38: 0.79: 0.734: 18.385: This course has helped me develop the ability to plan my own work: 3.18: 0.83: 2.52: 11.50: 0.804: 20.654: 0.891: Online classes has encouraged me to develop my own academic ...