e learning thesis research paper

ORIGINAL RESEARCH article

E-learning research trends in higher education in light of covid-19: a bibliometric analysis.

• 1 University of Bisha, Bisha, Saudi Arabia
• 2 University of Oum El Bouaghi, Oum El Bouaghi, Algeria
• 3 Binghamton University, Binghamton, NY, United States
• 4 University of Rochester, Rochester, NY, United States

This paper provides a broad bibliometric overview of the important conceptual advances that have been published during COVID-19 within “e-learning in higher education.” E-learning as a concept has been widely used in the academic and professional communities and has been approved as an educational approach during COVID-19. This article starts with a literature review of e-learning. Diverse subjects have appeared on the topic of e-learning, which is indicative of the dynamic and multidisciplinary nature of the field. These include analyses of the most influential authors, of models and networks for bibliometric analysis, and progress towards the current research within the most critical areas. A bibliometric review analyzes data of 602 studies published (2020–2021) in the Web of Science (WoS) database to fully understand this field. The data were examined using VOSviewer, CiteSpace, and KnowledgeMatrix Plus to extract networks and bibliometric indicators about keywords, authors, organizations, and countries. The study concluded with several results within higher education. Many converging words or sub-fields of e-learning in higher education included distance learning, distance learning, interactive learning, online learning, virtual learning, computer-based learning, digital learning, and blended learning (hybrid learning). This research is mainly focused on pedagogical techniques, particularly e-learning and collaborative learning, but these are not the only trends developing in this area. The sub-fields of artificial intelligence, machine learning, and deep learning constitute new research directions for e-learning in light of COVID-19 and are suggestive of new approaches for further analysis.

Introduction

The idea of e-learning was originated in the 1990s to explain learning thoroughly through technical advances. When instructional architecture and technologies have advanced, more attention has been paid to studying with the pedagogy. University education, further education, and e-learning have also recently adopted prominent roles in e-learning, too. It is now possible to provide e-learning for off-the-formal training through the internet. It also increased the need for personalization and advanced social people’s tools ( Siemens, 2005 ). In addition, it is often referred to as being able to read. It will help mix much learning more conveniently, but it has to be done, given the success of “traditional” e-learning pages. When the educational and technological assets join, this will be something more than a personal matter.

The COVID-19 pandemic has forced the closure of many activities, especially educational activities. To limit the spread of the pandemic, universities, institutes, and academic schools had to switch to e-learning using the available educational platforms. Social distancing is critical, and the COVID-19 pandemic has brought an end to face-to-face education, negatively impacting educational activities ( Maatuk et al., 2021 ). This closure has stimulated the growth of distance education activities as an alternative to face-to-face education in their various forms. Accordingly, many universities have shared the best ways to deliver course materials remotely, engage students, and conduct assessments.

The concept of e-learning, although widely known has not yet been fully explored ( Nicholson, 2007 ). Many countries designed and deployed distance education systems during the COVID-19 pandemic to ensure that higher education could continue without interruption ( Tesar, 2020 ). Several opportunities and challenges related to e-learning, higher education, and COVID-19 arose as a result of this, prompting a flurry of research into the area. When looking at the scientific studies published during the COVID-19 pandemic, it shows clearly that many international journals have published a large number of academic articles about e-learning in higher education during COVID-19 ( Karakose and Demirkol, 2021 ). Furthermore, a vast amount of bibliometric research has been carried out in this field. However, there is very little research focused entirely on the relationship between e-learning, higher education, and COVID-19, using scientometric or bibliometric analysis ( Furstenau et al., 2021 ).

This paper will discuss bibliometric indicators for e-learning in higher education during COVID-19 studies and proceed with a network analysis to define the most important sub-areas in this topic. To define the trends of e-learning in higher education during COVID-19, the following questions are proposed:

Q1: What are the most important sub-fields of e-learning in higher education in light of COVID-19?

Q2: Who are the most influential authors on the subject of e-learning in higher education in light of COVID-19?

Q3: What countries and research institutions are the most referenced for research on the subject of e-learning in higher education in light of COVID-19?

Q4: What are the research gaps and recent trends in the subject of e-learning in higher education in light of COVID-19?

An analysis was conducted to provide a broad and long-term perspective on the vocabulary of learning publications. It helps to recognize emerging problems within the multifaceted and increasing study fields of the world of e-learning. Newly published studies can improve knowledge and bridge the knowledge gap through findings regarding e-learning trends; this applies particularly to higher education due to the importance of knowing the latest information about distance learning and its methods. For this reason, the research is valuable for analyzing the volume of publications that have been made on the subject matter and to solidify the knowledge base on what has been studied by different expert researchers in education. So this will create new progress and new proposals to improve education in the event of a future pandemic.

In recent years, there has been an increasing interest in research within areas related to e-learning: online learning, blended learning, technology acceptance model, smart learning, interactive learning environments, intelligent tutoring systems, digital learning were reported ( Oprea, 2014 ; Castro-Schez et al., 2020 ; de Moura et al., 2020 ; Kao, 2020 ; Nylund and Lanz, 2020 ; Pal and Vanijja, 2020 ; Patricia, 2020 ; Şerban and Ioan, 2020 ).

A substantial quantity of literature has been written and published on the bibliometric analysis of e-learning. These studies mainly aim to identify the most critical areas (keywords) of e-learning. Networks such as that conducted by Chiang et al. (2010) showed that the significant research areas in e-learning are as follows: Education and Educational Research, Information Science and Library Science, and Computer.

Science/Multidisciplinary Applications

Cheng et al. (2014) analyzed data from 324 articles published between 2000 and 2012 in academic journals and conference proceedings from 2000 to 2012 to determine the vital research areas (the results identify six research themes in the field e-learning). Tibaná-Herrera et al. (2018a) used VOSViewer to conduct a bibliometric analysis of SCOPUS and SCImago Journal & Country Rank to establish the “e-learning” thematic category of scientific publications, thereby contributing to the discipline’s consolidation, accessibility, and development by researchers.

Bai et al. (2020) have also pursued similar work in analyzing 7,214 articles published in 10 journals on the subject of e-learning from 1999 to 2018; this study offers valuable hints on the future direction of how e-learning may evolve. Fatima and Abu (2019) examined 9,826 records from the Web of Science (WoS) database between 1989 to 2018 to identify significant contributions to the area of e-learning. The findings of this study show that the United States and the United Kingdom have contributed more than half of the research in e-Learning. According to a recent survey by Mashroofa et al. (2020) , the University of London is the most prolific institution globally. According to the WoS database, the institution has published 131 studies on e-learning; the bibliometric analysis of 6,934 results revealed that the publications received 59,784 citations.

Hung (2012) employed text mining and bibliometrics to examine 689 refereed journal articles and proceedings, comparing them to these research results. These works are divided into two domains, each of which has four groups. The study’s findings now offer evidence that e-learning methods vary across top countries and early adopter countries.

There have been multiple previous attempts to do a systematic review of e-learning publications ( Lahti et al., 2014 ; Zare et al., 2016 ; Garcia et al., 2018 ; Rodrigues et al., 2019 ; Araka et al., 2020 ; Valverde-Berrocoso et al., 2020 ), these studies mainly aimed to identify research areas, the most used and most important methods, and tools in e-learning.

Many studies have examined the results of e-learning publications through meta-analysis ( ŠUmak et al., 2011 ; Lahti et al., 2014 ; Mothibi, 2015 ; Cabero-Almenara et al., 2016 ; Yuwono and Sujono, 2018 ).

The study’s contribution is that no controlled studies have compared differences in networks, models, and software outputs to define the most critical research areas in e-learning and the most influenced authors, organizations, and countries.

The study makes an important contribution to the analysis of current models and networks of e-learning in higher education during the COVID-19 pandemic, aiming to define the most critical research areas in e-learning and the most influenced authors, organizations, and countries. In addition, it looks at the framework of e-learning and its future research trends in light of COVID-19. This has been done through numerous investigations ( Tibaná-Herrera et al., 2018a , c ; Hilmi and Mustapha, 2020 ; López-Belmonte et al., 2021 ).

Materials and Methods

Bibliometric data.

We retrieved published research via a topic search of the Science e-learning in higher education during the COVID-19 pandemic using the WoS database on August 12, 2021. The following search terms were used: topic = (“e-learning” “COVID-19” “higher education”), in title-abs-key from 2020 to 2021, and were 602 studies (475 articles, 80 articles; early access, 25 proceedings paper, 22 reviews) distributed over 2 years, as shown in Figure 1 .

Figure 1. Publications per year (KnowledgeMatrix Plus outputs).

The following selection criteria were used to choose the studies. First, for the title, we looked at the following: the studies that looked at the topic of e-learning in higher education during COVID-19. Second, for the abstract, we looked at the following: the studies that addressed the problem of e-learning in COVID-19. Third, for the keyword, we looked at the following: the studies that included e-learning, higher education, universities, and COVID-19. Fourth, the subject areas were limited to a selection of works that dealt with this subject in the following disciplines: business management and accounting, educational sciences, social sciences, and psychology.

The bibliometric study data represents the overall research on “E-learning in higher education in light of the COVID-19” in the WoS database. These data covered the last 2 years (2020 and 2021) in which the use of e-learning was expected due to the closure and quarantine procedures.

The reasons for choosing this database over others, particularly Scopus and ScienceDirect, are due to several considerations; due to WoS data, the field of scientometrics has advanced significantly. WoS is more than simply a database of academic papers. Many information objectives are supported by this selected, organized, and balanced database, including full citation links and improved metadata ( Birkle et al., 2020 ). WoS databases include high-quality research covering Science Citation Index Expanded (SCI-Expanded), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Emerging Sources Citation Index (ESCI) ( Falagas et al., 2008 ).

Figure 1 illustrates how interest in e-learning research has increased in recent years, particularly between 2020 and 2021. Among the 602 studies with 4,280 citations, 230 in 2020 (1,400 citations), and 372 in 2021 (2,880 citations), the importance of higher education institutions, including universities, in this modern teaching and learning approach and their significance in the educational process during COVID-19 is evident. They are different from the periods approved in the previous studies ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ). Therefore, this field of research (e-learning) has been renewed, and researchers should pay more attention to it to provide effective methods and approaches in light of the continuing epidemic.

Methods and Tools

According to the methods and approaches of bibliometric analysis (see: Zupic and Čater, 2015 , p. 04). the study relied on the co-occurrence indicator (co-word) to find out the main keywords on which previous studies focused as well as the co-authorship, publications, and citations indicators to find prominent authors, organizations, and countries in the topic of e-learning in higher education in COVID-19.

Following the methodology of preparing the bibliometric study in management and organization, which was explained by Zupic and Čater (2015) , the bibliometric analysis was carried out by completing the following steps: research design, study questions, and analysis approach selection (co-occurrence, publication, citation, and co-authorship); bibliometric data compilation, selection, and filtration, analysis (choosing the appropriate bibliometric software, clean the data, and generate networks); visualization, and interpretation.

The bibliometric analysis was performed to design networks of e-learning and define the most frequent keywords and the most cited authors, organizations, and countries to explain new and current trends within this topic. This is achieved depending on different software: CiteSpace converts research domain concepts into mapping functions between research frontiers and intellectual bases and is effective for information visualization ( Chen, 2016 ); VOSviewer is used to design the networks and is a powerful function for co-occurrence analysis and citation analysis ( Van Eck and Waltman, 2013 ). KnowledgeMatrix Plus is a powerful tool for analyzing frequency and statistics ( Chen and Song, 2017 ). This software was not used in previous studies ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ).

Results and Discussion

Keywords frequency.

Figures 2A,B and Supplementary Table 1 present the most frequent keywords that have been repeated more than five, which amounted to 131.

Figure 2. (A) Network of keywords (VOSviewer outputs). (B) Network of keywords (CiteSpace outputs).

Figure 2A shows nine sub-areas (clusters) for research in e-learning within higher education during the era of COVID-19. First, the red cluster shows searches related to the following: higher education, students, motivation, attitudes, systems, technology acceptance model, and user acceptance. Second, the green cluster shows searches related to the pandemic, blended learning, online learning, hybrid learning, flipped classrooms, virtual learning, and distance education. Third, the navy-blue cluster shows searches related to higher education online, online teaching, online assessment, formative assessment. Fourth, the yellow cluster relates to stress, health, care, quarantine, mental health, anxiety, college students, adults, children. Fifth, the violet cluster shows searches related to surgery, surgical education, skills, strategies, student satisfaction, and simulation. Sixth, the light blue cluster shows searches related to e-learning, performance, quality, remote learning, digital learning, assessment, evaluation. Seventh, the orange cluster shows searches related to education, Covid-19, coronavirus, sars-cov-2, distance learning, medical education. Eighth, the brown cluster included: computer-based learning, self-instruction/distance learning, internet/web-based education, curriculum, knowledge, science, and technology. Finally, the pink clusters showed searches related to artificial intelligence, machine learning, and deep learning. The researcher can also take these subfields as topics for research in e-learning, especially the last cluster, which formed a recent research trend for many scholars ( Bhardwaj et al., 2021 ; Kashive et al., 2021 ; Rasheed and Wahid, 2021 ).

Figure 2B shows that the research on this topic requires focusing on several issues. These are the most frequently mentioned keywords in Supplementary Table 1 , including COVID-19 crisis, technology acceptance model (TAM), distance education, stress, ICT, special education needs, mental health, student satisfaction, surgical teaching, self-efficacy, technology adoption, using the machine, and e-learning. At the same time, many studies used different terms to express the same meaning, such as interactive learning, online learning, and Distance learning. This is similar to what was found in previous studies on e-learning ( Chiang et al., 2010 ; Cheng et al., 2014 ; Bai et al., 2020 ; Fatimah et al., 2021 ).

Reference Authors

Figures 3A–C show the network of the most referenced authors on the topic of “E-learning in higher education in COVID-19” based on co-authorship:

Figure 3. (A) Network of authors (VOSviewer outputs). (B) Publications and citations per author (KnowledgeMatrix Plus outputs). (C) Network of cited authors in COVID-19 (CiteSpace outputs).

Figure 3A shows that there is a research partnership between eight authors. The co-authorship is the affiliation and the country: Fernando Augusto Bozza, Rosana Souza Rodrigues, Walter Araujo Zin, Alan Guimaraes and Gabriel Madeira Werberich, Federal University of Rio de Janeiro, Brazil. Joana Sofia F. Pinto, Willian Reboucas Schmitt and Manuela Franca, Complexo Hosp Univ Porto, Radiol Dept, Porto, Portugal. As for the rest, they have separate and individual publications. Figures 3A–C present the top authors based on publications and citations.

Figure 3B shows that the first author on this topic on “E-learning in higher education in COVID-19” is Antonio José Moreno-Guerrero, Univ Granada, Dept Didact & Sch Org, Spain. Among this research, we find “Impact of Educational Stage in the Application of Flipped Learning: A Contrasting Analysis with Traditional Teaching” ( Pozo Sánchez et al., 2019 ). We also find research on e-learning in mathematics teaching: an educational experience in adult high school ( Moreno-Guerrero et al., 2020 ) as well as research on the following: the effectiveness of innovating educational practices with flipped learning and remote sensing in earth and environmental sciences ( López Núñez et al., 2020 ); machine learning and big data and their impact on literature; a bibliometric review with scientific mapping in WoS; and a flipped learning approach as an educational innovation in water literacy ( López Belmonte et al., 2020 ; López Núñez et al., 2020 ). Moreno-Guerrero talked about e-learning and did not discuss the COVID-19 ( Moreno-Guerrero et al., 2020 ); otherwise, Lüftenegger discussed e-learning and COVID-19 ( Holzer et al., 2021 ; Korlat et al., 2021 ; Pelikan et al., 2021 ).

Figure 3C shows that the most important authors searched in COVID-19 and touched on e-learning are Maram Meccawy, Isabel Chiyon, and Anand Nayyar among others.

Reference Organizations

Figures 4A–C displays the most referenced organizations on the topic of “E-learning in higher education in COVID-19” based on publications, citations, and co-authorship.

Figure 4. (A) Network of organizations (VOSviewer outputs). (B) Network of organizations (CiteSpace outputs). (C) Citations per publications by the organization (KnowledgeMatrix Plus outputs).

Figures 4A–C demonstrate that the leading research organization for publications, citations, and co-authorship on this topic is the University of Toronto with 16 publications and 207 citations, followed by the University of King Abdulaziz with 15 publications and 57 citations the Jordan University of Science and Technology with 11 publications and 115 citations, then the University of Vienna with 10 publications and 30 citations, then the University of Sharjah with 10 publications and 20 citations, then the University of Granada with 9 publications and 79 citations, then the University of Porto with 9 publications and 14 citations, then Monterrey Institute of Technology and Graduate Studies with 9 publications and 2 citations, then the University of Jordan with 8 publications and 46 citations, and finally, the University of Colorado with 8 publications and 16 citations. That is due to several reasons, including the interest of these organizations in publishing in the WoS database. Then their interest in publishing in the subject of the study. We thus find it among the top 500 universities. 1

Reference Countries

Figures 5A–C display the most referenced countries on the topic of “E-learning in higher education in COVID-19” based on publications, citations, and co-authorship.

Figure 5. (A) Network of countries (VOSviewer outputs). (B) Network of countries (CiteSpace outputs). (C) Citations per publications by country (KnowledgeMatrix Plus outputs).

Figures 5A–C illustrate that the top countries for publications, citations, and co-authorship in this topic are as follows: the United States with 344 publications and 1,167 citations, the United Kingdom with 132 publications and 530 citations, China with 117 publications and 592 citations, Spain with 104 publications and 321 citations, Italy with 98 publications and 175 citations, Brazil with 74 publications and 224 citations, Canada with 67 publications and 368 citations, India with 64 publications and 139 citations, Saudi Arabia with 60 publications and 216 citations, and Germany with 59 publications and 133 citations. These show extensive collaboration, especially between the United States and the United Kingdom with 11 collaborations, between the United States and Canada with 10 collaborations, and between the United States and China with 9 collaborations; other countries show an average of 3–5 collaborations.

The results of the bibliometric analysis showed that there are nine sub-fields of research within a topic: motivation and students’ attitudes to e-learning systems in higher education (technology acceptance model), comparison between blended learning and virtual learning, online assessment versus formative assessment of students in higher education, stress, anxiety, and mental health of college students in COVID-19, surgical education strategies to develop students’ skills, quality and performance of higher education strategies of e-learning in COVID-19, challenges of medical education and distance learning during COVID-19, and changing higher education curricula using technology.

Finally, using artificial intelligence, machine learning, and deep learning to transform the e-learning Industry, this final sub-field formed a recent research trend for many scholars ( Bhardwaj et al., 2021 ; Kashive et al., 2021 ; Rasheed and Wahid, 2021 ).

The bibliometric study shows that the first author in e-learning is Antonio José Moreno-Guerrero, Univ Granada, Dept Didact & Sch Org, and Spain. His writings ( Pozo Sánchez et al., 2019 ; López Núñez et al., 2020 ; Moreno-Guerrero et al., 2020 ) are considered a useful reference in e-learning and blended learning. Therefore, Marko Lüftenegger is one of the most influential author in the topic of “E-learning in higher education in COVID-19” ( Holzer et al., 2021 ; Korlat et al., 2021 ; Pelikan et al., 2021 )

The results of the bibliometric analysis showed that the top research organizations in this domain are as follows: the University of Toronto, the University of King Abdulaziz, Jordan University of Science and Technology, the University of Vienna, the University of Sharjah, the University of Granada, the University of Porto, Monterrey Institute of Technology and Graduate Studies, the University of Jordan, and the University of Colorado. The results also illustrate that the top countries are: United States, United Kingdom, China, Spain, Italy, Brazil, Canada, India, Saudi Arabia, Germany, due to several reasons, including the interest of these organizations and countries in publishing in the Web of Science database and their interest in publishing in the subject of the study.

Our research overlaps with that of López-Belmonte et al. (2021) , who tried to investigate the development of e-learning in higher education in the academic literature listed on the WoS. The same analysis, as well as bibliometric analysis, was carried out. The findings revealed no set path for research because of the research on e-learning in higher education, recent creation, and a scarcity of relevant research. According to the results of the bibliometric analysis, the study was aimed at determining acceptance and implementation of the educational curriculum in the teaching and learning processes.

This paper discusses the use of a bibliometric approach to track e-learning trends in higher education during the COVID-19 pandemic through the WoS database. From a methodological perspective, our proposed approach can visually represent the temporal links of the most cited articles internally in various streams and provide a comprehensive overview of the evolution of topics in the WoS database. Also, direct citation network analysis enables researchers to test articles important in e-learning and get a comprehensive overview of the issues published.

The study provided an insight into the world’s e-learning research in terms of mapping research publications. A scientific study was conducted using 602 e-learning documents from 2020 to 2021, and these were obtained through the WoS database. Over the years, the analysis identified trends in contributions in this area and headline sources for most researchers and leading institutions. The study is convergent with many previous studies in this area, including Chiang et al. (2010) , Hung (2012) , Cheng et al. (2014) , Tibaná-Herrera et al. (2018b) , Fatima and Abu (2019) , Bai et al. (2020) , and Mashroofa et al. (2020) . However, our study relies on many software to compare various theoretical models and networks of e-learning.

Based on the analysis data’s inference, growth trends in research publishing in e-learning of different forms have increased in recent years, especially so for the last 2 years (230 in 2020 and 386 in 2021). The significant findings of the bibliometric analysis are as follows: there are nine sub-fields of study in the subject of “E-learning in higher education in COVID-19,” and the prominent authors in this area are as follows: Antonio José Moreno-Guerrero and Marko Lüftenegger; the University of Toronto Canada is the most frequently cited organization in this domain; the United States is the leading country in terms of publications and citations; and the sub-field of artificial intelligence, machine learning, and deep learning to transform the eLearning Industry has emerged as a recent research trend for many scholars.

The study examined a very important topic, which is one of the current topics, “e-learning in higher education during COVID-19,” using bibliometric analysis of 602 studies published in Web of Science databases from 2020 to 2021. We found that the study sample should be larger; it needs further studies and a longer time, especially when we analyze citation, and research on this topic will thus continue in future years. Also, there are many tools and methods used in the bibliometric analysis that were not used in our study, including what has been mentioned ( Tibaná-Herrera et al., 2018b ; Gul et al., 2020 ; López-Belmonte et al., 2021 ; Rashid et al., 2021 ).

The findings of this study will assist interested academics and educational policymakers ( Brika et al., 2021 ) in the field of e-learning in understanding the current state of e-learning and identifying the different research trends in light of COVID-19. Additionally, it will serve as the beginning point for new research during the COVID-19 crisis, which will examine various problems and trends.

The findings of this research may help evaluate e-learning institutions’ quality and promote future educational trends. The findings may be utilized by e-learning institutions to evaluate quality as strategic dimensions and policy makers’ vision.

Data Availability Statement

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

Author Contributions

All authors contributed to the design and implementation of the research, performed the revision, verified the analytical methods, supervised the findings of this work, discussed the results, and contributed to the final manuscript.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Acknowledgments

The authors extend their appreciation to the Deputyship for Research & Innovation, Ministry of Education in Saudi Arabia, to fund this research work through the project number (UB-56-1442).

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyg.2021.762819/full#supplementary-material

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Keywords : e-learning, higher education, COVID-19, bibliometric analysis, Web of Science (WoS) database

Citation: Brika SKM, Chergui K, Algamdi A, Musa AA and Zouaghi R (2022) E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis. Front. Psychol. 12:762819. doi: 10.3389/fpsyg.2021.762819

Received: 22 August 2021; Accepted: 31 December 2021; Published: 03 March 2022.

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Copyright © 2022 Brika, Chergui, Algamdi, Musa and Zouaghi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Said Khalfa Mokhtar Brika, [email protected]

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• Research article
• Open access
• Published: 01 October 2021

Adaptive e-learning environment based on learning styles and its impact on development students' engagement

• Hassan A. El-Sabagh   ORCID: orcid.org/0000-0001-5463-5982 1 , 2  

International Journal of Educational Technology in Higher Education volume  18 , Article number:  53 ( 2021 ) Cite this article

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Adaptive e-learning is viewed as stimulation to support learning and improve student engagement, so designing appropriate adaptive e-learning environments contributes to personalizing instruction to reinforce learning outcomes. The purpose of this paper is to design an adaptive e-learning environment based on students' learning styles and study the impact of the adaptive e-learning environment on students’ engagement. This research attempts as well to outline and compare the proposed adaptive e-learning environment with a conventional e-learning approach. The paper is based on mixed research methods that were used to study the impact as follows: Development method is used in designing the adaptive e-learning environment, a quasi-experimental research design for conducting the research experiment. The student engagement scale is used to measure the following affective and behavioral factors of engagement (skills, participation/interaction, performance, emotional). The results revealed that the experimental group is statistically significantly higher than those in the control group. These experimental results imply the potential of an adaptive e-learning environment to engage students towards learning. Several practical recommendations forward from this paper: how to design a base for adaptive e-learning based on the learning styles and their implementation; how to increase the impact of adaptive e-learning in education; how to raise cost efficiency of education. The proposed adaptive e-learning approach and the results can help e-learning institutes in designing and developing more customized and adaptive e-learning environments to reinforce student engagement.

Introduction

In recent years, educational technology has advanced at a rapid rate. Once learning experiences are customized, e-learning content becomes richer and more diverse (El-Sabagh & Hamed, 2020 ; Yang et al., 2013 ). E-learning produces constructive learning outcomes, as it allows students to actively participate in learning at anytime and anyplace (Chen et al., 2010 ; Lee et al., 2019 ). Recently, adaptive e-learning has become an approach that is widely implemented by higher education institutions. The adaptive e-learning environment (ALE) is an emerging research field that deals with the development approach to fulfill students' learning styles by adapting the learning environment within the learning management system "LMS" to change the concept of delivering e-content. Adaptive e-learning is a learning process in which the content is taught or adapted based on the responses of the students' learning styles or preferences. (Normadhi et al., 2019 ; Oxman & Wong, 2014 ). By offering customized content, adaptive e-learning environments improve the quality of online learning. The customized environment should be adaptable based on the needs and learning styles of each student in the same course. (Franzoni & Assar, 2009 ; Kolekar et al., 2017 ). Adaptive e-learning changes the level of instruction dynamically based on student learning styles and personalizes instruction to enhance or accelerate a student's success. Directing instruction to each student's strengths and content needs can minimize course dropout rates, increase student outcomes and the speed at which they are accomplished. The personalized learning approach focuses on providing an effective, customized, and efficient path of learning so that every student can participate in the learning process (Hussein & Al-Chalabi, 2020 ). Learning styles, on the other hand, represent an important issue in learning in the twenty-first century, with students expected to participate actively in developing self-understanding as well as their environment engagement. (Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Truong, 2016 ).

In current conventional e-learning environments, instruction has traditionally followed a “one style fits all” approach, which means that all students are exposed to the same learning procedures. This type of learning does not take into account the different learning styles and preferences of students. Currently, the development of e-learning systems has accommodated and supported personalized learning, in which instruction is fitted to a students’ individual needs and learning styles (Beldagli & Adiguzel, 2010 ; Benhamdi et al., 2017 ; Pashler et al., 2008 ). Some personalized approaches let students choose content that matches their personality (Hussein & Al-Chalabi, 2020 ). The delivery of course materials is an important issue of personalized learning. Moreover, designing a well-designed, effective, adaptive e-learning system represents a challenge due to complication of adapting to the different needs of learners (Alshammari, 2016 ). Regardless of using e-learning claims that shifting to adaptive e-learning environments to be able to reinforce students' engagement. However, a learning environment cannot be considered adaptive if it is not flexible enough to accommodate students' learning styles. (Ennouamani & Mahani, 2017 ).

On the other hand, while student engagement has become a central issue in learning, it is also an indicator of educational quality and whether active learning occurs in classes. (Lee et al., 2019 ; Nkomo et al., 2021 ; Robinson & Hullinger, 2008 ). Veiga et al. ( 2014 ) suggest that there is a need for further research in engagement because assessing students’ engagement is a predictor of learning and academic progress. It is important to clarify the distinction between causal factors such as learning environment and outcome factors such as achievement. Accordingly, student engagement is an important research topic because it affects a student's final grade, and course dropout rate (Staikopoulos et al., 2015 ).

The Umm Al-Qura University strategic plan through common first-year deanship has focused on best practices that increase students' higher-order skills. These skills include communication skills, problem-solving skills, research skills, and creative thinking skills. Although the UQU action plan involves improving these skills through common first-year academic programs, the student's learning skills need to be encouraged and engaged more (Umm Al-Qura University Agency, 2020 ). As a result of the author's experience, The conventional methods of instruction in the "learning skills" course were observed, in which the content is presented to all students in one style that is dependent on understanding the content regardless of the diversity of their learning styles.

According to some studies (Alshammari & Qtaish, 2019 ; Lee & Kim, 2012 ; Shih et al., 2008 ; Verdú, et al., 2008 ; Yalcinalp & Avc, 2019 ), there is little attention paid to the needs and preferences of individual learners, and as a result, all learners are treated in the same way. More research into the impact of educational technologies on developing skills and performance among different learners is recommended. This “one-style-fits-all” approach implies that all learners are expected to use the same learning style as prescribed by the e-learning environment. Subsequently, a review of the literature revealed that an adaptive e-learning environment can affect learning outcomes to fill the identified gap. In conclusion: Adaptive e-learning environments rely on the learner's preferences and learning style as a reference that supports to create adaptation.

To confirm the above: the author conducted an exploratory study via an open interview that included some questions with a sample of 50 students in the learning skills department of common first-year. Questions asked about the difficulties they face when learning a "learning skills" course, what is the preferred way of course content. Students (88%) agreed that the way students are presented does not differ according to their differences and that they suffer from a lack of personal learning that is compatible with their style of work. Students (82%) agreed that they lack adaptive educational content that helps them to be engaged in the learning process. Accordingly, the author handled the research problem.

This research supplements to the existing body of knowledge on the subject. It is considered significant because it improves understanding challenges involved in designing the adaptive environments based on learning styles parameter. Subsequently, this paper is structured as follows: The next section presents the related work cited in the literature, followed by research methodology, then data collection, results, discussion, and finally, some conclusions and future trends are discussed.

Theoretical framework

This section briefly provides a thorough review of the literature about the adaptive E-learning environments based on learning styles.

Adaptive e-learning environments based on learning styles

The adaptive e-learning employment in higher education has been slower to evolve, and challenges that led to the slow implementation still exist. The learning management system offers the same tools to all learners, although individual learners need different details based on learning style and preferences. (Beldagli & Adiguzel, 2010 ; Kolekar et al., 2017 ). The interactive e-learning environment requisite evaluating the learner's desired learning style, before the course delivery, such as an online quiz or during the course delivery, such as tracking student reactions (DeCapua & Marshall, 2015 ).

In e-learning environments, adaptation is constructed on a series of well-designed processes to fit the instructional materials. The adaptive e-learning framework attempt to match instructional content to the learners' needs and styles. According to Qazdar et al. ( 2015 ), adaptive e-learning (AEL) environments rely on constructing a model of each learner's needs, preferences, and styles. It is well recognized that such adaptive behavior can increase learners' development and performance, thus enriching learning experience quality. (Shi et al., 2013 ). The following features of adaptive e-learning environments can be identified through diversity, interactivity, adaptability, feedback, performance, and predictability. Although adaptive framework taxonomy and characteristics related to various elements, adaptive learning includes at least three elements: a model of the structure of the content to be learned with detailed learning outcomes (a content model). The student's expertise based on success, as well as a method of interpreting student strengths (a learner model), and a method of matching the instructional materials and how it is delivered in a customized way (an instructional model) (Ali et al., 2019 ). The number of adaptive e-learning studies has increased over the last few years. Adaptive e-learning is likely to increase at an accelerating pace at all levels of instruction (Hussein & Al-Chalabi, 2020 ; Oxman & Wong, 2014 ).

Many studies assured the power of adaptive e-learning in delivering e-content for learners in a way that fitting their needs, and learning styles, which helps improve the process of students' acquisition of knowledge, experiences and develop their higher thinking skills (Ali et al., 2019 ; Behaz & Djoudi, 2012 ; Chun-Hui et al., 2017 ; Daines et al., 2016 ; Dominic et al., 2015 ; Mahnane et al., 2013 ; Vassileva, 2012 ). Student characteristics of learning style are recognized as an important issue and a vital influence in learning and are frequently used as a foundation to generate personalized learning experiences (Alshammari & Qtaish, 2019 ; El-Sabagh & Hamed, 2020 ; Hussein & Al-Chalabi, 2020 ; Klasnja-Milicevic et al., 2011 ; Normadhi et al., 2019 ; Ozyurt & Ozyurt, 2015 ).

The learning style is a parameter of designing adaptive e-learning environments. Individuals differ in their learning styles when interacting with the content presented to them, as many studies emphasized the relationship between e-learning and learning styles to be motivated in learning situations, consequently improving the learning outcomes (Ali et al., 2019 ; Alshammari, 2016 ; Alzain et al., 2018a , b ; Liang, 2012 ; Mahnane et al., 2013 ; Nainie et al., 2010 ; Velázquez & Assar, 2009 ). The word "learning style" refers to the process by which the learner organizes, processes, represents, and combines this information and stores it in his cognitive source, then retrieves the information and experiences in the style that reflects his technique of communicating them. (Fleming & Baume, 2006 ; Jaleel & Thomas, 2019 ; Jonassen & Grabowski, 2012 ; Klasnja-Milicevic et al., 2011 ; Nuankaew et al., 2019 ; Pashler et al., 2008 ; Willingham et al., 2105 ; Zhang, 2017 ). The concept of learning style is founded based on the fact that students vary in their styles of receiving knowledge and thought, to help them recognizing and combining information in their mind, as well as acquire experiences and skills. (Naqeeb, 2011 ). The extensive scholarly literature on learning styles is distributed with few strong experimental findings (Truong, 2016 ), and a few findings on the effect of adapting instruction to learning style. There are many models of learning styles (Aldosarim et al., 2018 ; Alzain et al., 2018a , 2018b ; Cletus & Eneluwe, 2020 ; Franzoni & Assar, 2009 ; Willingham et al., 2015 ), including the VARK model, which is one of the most well-known models used to classify learning styles. The VARK questionnaire offers better thought about information processing preferences (Johnson, 2009 ). Fleming and Baume ( 2006 ) developed the VARK model, which consists of four students' preferred learning types. The letter "V" represents for visual and means the visual style, while the letter "A" represents for auditory and means the auditory style, and the letter "R/W" represents "write/read", means the reading/writing style, and the letter "K" represents the word "Kinesthetic" and means the practical style. Moreover, VARK distinguishes the visual category further into graphical and textual or visual and read/write learners (Murphy et al., 2004 ; Leung, et al., 2014 ; Willingham et al., 2015 ). The four categories of The VARK Learning Style Inventory are shown in the Fig. 1 below.

VARK learning styles

According to the VARK model, learners are classified into four groups representing basic learning styles based on their responses which have 16 questions, there are four potential responses to each question, where each answer agrees to one of the extremes of the dimension (Hussain, 2017 ; Silva, 2020 ; Zhang, 2017 ) to support instructors who use it to create effective courses for students. Visual learners prefer to take instructional materials and send assignments using tools such as maps, graphs, images, and other symbols, according to Fleming and Baume ( 2006 ). Learners who can read–write prefer to use written textual learning materials, they use glossaries, handouts, textbooks, and lecture notes. Aural learners, on the other hand, prefer to learn through spoken materials, dialogue, lectures, and discussions. Direct practice and learning by doing are preferred by kinesthetic learners (Becker et al., 2007 ; Fleming & Baume, 2006 ; Willingham et al., 2015 ). As a result, this research work aims to provide a comprehensive discussion about how these individual parameters can be applied in adaptive e-learning environment practices. Dominic et al., ( 2015 ) presented a framework for an adaptive educational system that personalized learning content based on student learning styles (Felder-Silverman learning model) and other factors such as learners' learning subject competency level. This framework allowed students to follow their adaptive learning content paths based on filling in "ils" questionnaire. Additionally, providing a customized framework that can automatically respond to students' learning styles and suggest online activities with complete personalization. Similarly, El Bachari et al. ( 2011 ) attempted to determine a student's unique learning style and then adapt instruction to that individual interests. Adaptive e-learning focused on learner experience and learning style has a higher degree of perceived usability than a non-adaptive e-learning system, according to Alshammari et al. ( 2015 ). This can also improve learners' satisfaction, engagement, and motivation, thus improving their learning.

According to the findings of (Akbulut & Cardak, 2012 ; Alshammari & Qtaish, 2019 ; Alzain et al., 2018a , b ; Shi et al., 2013 ; Truong, 2016 ), adaptation based on a combination of learning style, and information level yields significantly better learning gains. Researchers have recently initiated to focus on how to personalize e-learning experiences using personal characteristics such as the student's preferred learning style. Personal learning challenges are addressed by adaptive learning programs, which provide learners with courses that are fit to their specific needs, such as their learning styles.

• Student engagement

Previous research has emphasized that student participation is a key factor in overcoming academic problems such as poor academic performance, isolation, and high dropout rates (Fredricks et al., 2004 ). Student participation is vital to student learning, especially in an online environment where students may feel isolated and disconnected (Dixson, 2015 ). Student engagement is the degree to which students consciously engage with a course's materials, other students, and the instructor. Student engagement is significant for keeping students engaged in the course and, as a result, in their learning (Barkley & Major, 2020 ; Lee et al., 2019 ; Rogers-Stacy, et al, 2017 ). Extensive research was conducted to investigate the degree of student engagement in web-based learning systems and traditional education systems. For instance, using a variety of methods and input features to test the relationship between student data and student participation (Hussain et al., 2018 ). Guo et al. ( 2014 ) checked the participation of students when they watched videos. The input characteristics of the study were based on the time they watched it and how often students respond to the assessment.

Atherton et al. ( 2017 ) found a correlation between the use of course materials and student performance; course content is more expected to lead to better grades. Pardo et al., ( 2016 ) found that interactive students with interactive learning activities have a significant impact on student test scores. The course results are positively correlated with student participation according to previous research. For example, Atherton et al. ( 2017 ) explained that students accessed learning materials online and passed exams regularly to obtain higher test scores. Other studies have shown that students with higher levels of participation in questionnaires and course performance tend to perform well (Mutahi et al., 2017 ).

Skills, emotion, participation, and performance, according to Dixson ( 2015 ), were factors in online learning engagement. Skills are a type of learning that includes things like practicing on a daily foundation, paying attention while listening and reading, and taking notes. Emotion refers to how the learner feels about learning, such as how much you want to learn. Participation refers to how the learner act in a class, such as chat, discussion, or conversation. Performance is a result, such as a good grade or a good test score. In general, engagement indicated that students spend time, energy learning materials, and skills to interact constructively with others in the classroom, and at least participate in emotional learning in one way or another (that is, be motivated by an idea, willing to learn and interact). Student engagement is produced through personal attitudes, thoughts, behaviors, and communication with others. Thoughts, effort, and feelings to a certain level when studying. Therefore, the student engagement scale attempts to measure what students are doing (thinking actively), how they relate to their learning, and how they relate to content, faculty members, and other learners including the following factors as shown in Fig.  2 . (skills, participation/interaction, performance, and emotions). Hence, previous research has moved beyond comparing online and face-to-face classes to investigating ways to improve online learning (Dixson, 2015 ; Gaytan & McEwen, 2007 ; Lévy & Wakabayashi, 2008 ; Mutahi et al., 2017 ). Learning effort, involvement in activities, interaction, and learning satisfaction, according to reviews of previous research on student engagement, are significant measures of student engagement in learning environments (Dixson, 2015 ; Evans et al., 2017 ; Lee et al., 2019 ; Mutahi et al., 2017 ; Rogers-Stacy et al., 2017 ). These results point to several features of e-learning environments that can be used as measures of student participation. Successful and engaged online learners learn actively, have the psychological inspiration to learn, make good use of prior experience, and make successful use of online technology. Furthermore, they have excellent communication abilities and are adept at both cooperative and self-directed learning (Dixson, 2015 ; Hong, 2009 ; Nkomo et al., 2021 ).

Engagement factors

Overview of designing the adaptive e-learning environment

The paper follows the (ADDIE) Instructional Design Model: analysis, design, develop, implement, and evaluate to answer the first research question. The adaptive learning environment offers an interactive decentralized media environment that takes into account individual differences among students. Moreover, the environment can spread the culture of self-learning, attract students, and increase their engagement in learning.

Any learning environment that is intended to accomplish a specific goal should be consistent to increase students' motivation to learn. so that they have content that is personalized to their specific requirements, rather than one-size-fits-all content. As a result, a set of instructional design standards for designing an adaptive e-learning framework based on learning styles was developed according to the following diagram (Fig. 3 ).

The ID (model) of the adaptive e-learning environment

According to the previous figure, The analysis phase included identifying the course materials and learning tools (syllabus and course plan modules) used for the study. The learning objectives were included in the high-level learning objectives (C4-C6: analysis, synthesis, evaluation).

The design phase included writing SMART objectives, the learning materials were written within the modules plan. To support adaptive learning, four content paths were identified, choosing learning models, processes, and evaluation. Course structure and navigation were planned. The adaptive structural design identified the relationships between the different components, such as introduction units, learning materials, quizzes. Determining the four path materials. The course instructional materials were identified according to the following Figure 4 .

Adaptive e-course design

The development phase included: preparing and selecting the media for the e-course according to each content path in an adaptive e-learning environment. During this process, the author accomplished the storyboard and the media to be included on each page of the storyboard. A category was developed for the instructional media for each path (Fig. 5 )

Roles and deployment diagram of the adaptive e-learning environment

The author developed a learning styles questionnaire via a mobile App. as follows: https://play.google.com/store/apps/details?id=com.pointability.vark . Then, the students accessed the adaptive e-course modules based on their learning styles.

The Implementation phase involved the following: The professional validation of the course instructional materials. Expert validation is used to evaluate the consistency of course materials (syllabi and modules). The validation was performed including the following: student learning activities, learning implementation capability, and student reactions to modules. The learner's behaviors, errors, navigation, and learning process are continuously geared toward improving the learner's modules based on the data the learner gathered about him.

The Evaluation phase included five e-learning specialists who reviewed the adaptive e-learning. After that, the framework was revised based on expert recommendations and feedback. Content assessment, media evaluation in three forms, instructional design, interface design, and usage design included in the evaluation. Adaptive learners checked the proposed framework. It was divided into two sections. Pilot testing where the proposed environment was tested by ten learners who represented the sample in the first phase. Each learner's behavior was observed, questions were answered, and learning control, media access, and time spent learning were all verified.

Research methodology

Research purpose and questions.

This research aims to investigate the impact of designing an adaptive e-learning environment on the development of students' engagement. The research conceptual framework is illustrated in Fig.  6 . Therefore, the articulated research questions are as follows: the main research question is "What is the impact of an adaptive e-learning environment based on (VARK) learning styles on developing students' engagement? Accordingly, there are two sub research questions a) "What is the instructional design of the adaptive e-learning environment?" b) "What is the impact of an adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation, performance, emotional) in comparison with conventional e-learning?".

The conceptual framework (model) of the research questions

Research hypotheses

The research aims to verify the validity of the following hypothesis:

There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale.

There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group.

Research design

This research was a quasi-experimental research with the pretest-posttest. Research variables were independent and dependent as shown in the following Fig. 7 .

Research "Experimental" design

Both groups were informed with the learning activities tracks, the experimental group was instructed to use the adaptive learning environment to accomplish the learning goals; on the other hand, the control group was exposed to the conventional e-learning environment without the adaptive e-learning parameters.

Research participants

The sample consisted of students studying the "learning skills" course in the common first-year deanship aged between (17–18) years represented the population of the study. All participants were chosen in the academic year 2109–2020 at the first term which was taught by the same instructors. The research sample included two classes (118 students), selected randomly from the learning skills department. First-group was randomly assigned as the control group (N = 58, 31 males and 27 females), the other was assigned as experimental group (N = 60, 36 males and 24 females) was assigned to the other class. The following Table 1 shows the distribution of students' sample "Demographics data".

The instructional materials were not presented to the students before. The control group was expected to attend the conventional e-learning class, where they were provided with the learning environment without adaptive e-learning parameter based on the learning styles that introduced the "learning skills" course. The experimental group was exposed to the use of adaptive e-learning based on learning styles to learn the same course instructional materials within e-course. Moreover, all the student participants were required to read the guidelines to indicate their readiness to participate in the research experiment with permission.

Research instruments

In this research, the measuring tools included the VARK questionnaire and the students' engagement scale including the following factors (skills, participation/interaction, performance, emotional). To begin, the pre-post scale was designed to assess the level of student engagement related to the "learning skills" course before and after participating in the experiment.

VARK questionnaire

Questionnaires are a common method for collecting data in education research (McMillan & Schumacher, 2006 ). The VARK questionnaire had been organized electronically and distributed to the student through the developed mobile app and registered on the UQU system. The questionnaire consisted of 16 items within the scale as MCQ classified into four main factors (kinesthetic, auditory, visual, and R/W).

Reliability and Validity of The VARK questionnaire

For reliability analysis, Cronbach’s alpha is used for evaluating research internal consistency. Internal consistency was calculated through the calculation of correlation of each item with the factor to which it fits and correlation among other factors. The value of 0.70 and above are normally recognized as high-reliability values (Hinton et al., 2014 ). The Cronbach's Alpha correlation coefficient for the VARK questionnaire was 0.83, indicating that the questionnaire was accurate and suitable for further research.

Students' engagement scale

The engagement scale was developed after a review of the literature on the topic of student engagement. The Dixson scale was used to measure student engagement. The scale consisted of 4 major factors as follows (skills, participation/interaction, performance, emotional). The author adapted the original "Dixson scale" according to the following steps. The Dixson scale consisted of 48 statements was translated and accommodated into Arabic by the author. After consulting with experts, the instrument items were reduced to 27 items after adaptation according to the university learning environment. The scale is rated on a 5-point scale.

The final version of the engagement scale comprised 4 factors as follows: The skills engagement included (ten items) to determine keeping up with, reading instructional materials, and exerting effort. Participation/interaction engagement involved (five items) to measure having fun, as well as regularly engaging in group discussion. The performance engagement included (five items) to measure test performance and receiving a successful score. The emotional engagement involved (seven items) to decide whether or not the course was interesting. Students can access to respond engagement scale from the following link: http://bit.ly/2PXGvvD . Consequently, the objective of the scale is to measure the possession of common first-year students of the basic engagement factors before and after instruction with adaptive e-learning compared to conventional e-learning.

Reliability and validity of the engagement scale

The alpha coefficient of the scale factors scores was presented. All four subscales have a strong degree of internal accuracy (0.80–0.87), indicating strong reliability. The overall reliability of the instruments used in this study was calculated using Alfa-alpha, Cronbach's with an alpha value of 0.81 meaning that the instruments were accurate. The instruments used in this research demonstrated strong validity and reliability, allowing for an accurate assessment of students' engagement in learning. The scale was applied to a pilot sample of 20 students, not including the experimental sample. The instrument, on the other hand, had a correlation coefficient of (0.74–0.82), indicating a degree of validity that enables the instrument's use. Table 2 shows the correlation coefficient and Cronbach's alpha based on the interaction scale.

On the other hand, to verify the content validity; the scale was to specialists to take their views on the clarity of the linguistic formulation and its suitability to measure students' engagement, and to suggest what they deem appropriate in terms of modifications.

Research procedures

To calculate the homogeneity and group equivalence between both groups, the validity of the first hypothesis was examined which stated "There is no statistically significant difference between the students' mean scores of the experimental group that exposed to the adaptive e-learning environment and the scores of the control group that was exposed to the conventional e-learning environment in pre-application of students' engagement scale", the author applied the engagement scale to both groups beforehand, and the scores of the pre-application were examined to verify the equivalence of the two groups (experimental and control) in terms of students' engagement.

The t-test of independent samples was calculated for the engagement scale to confirm the homogeneity of the two classes before the experiment. The t-values were not significant at the level of significance = 0.05, meaning that the two groups were homogeneous in terms of students' engagement scale before the experiment.

Since there was no significant difference in the mean scores of both groups ( p  > 0.05), the findings presented in Table 3 showed that there was no significant difference between both experimental and control groups in engagement as a whole, and each student engagement factor separately. The findings showed that the two classes were similar before start of research experiment.

Learner content path in adaptive e-learning environment

The previous well-designed processes are the foundation for adaptation in e-learning environments. There are identified entries for accommodating materials, including classification depending on learning style.: kinesthetic, auditory, visual, and R/W. The present study covered the 1st semester during the 2019/2020 academic year. The course was divided into modules that concentrated on various topics; eleven of the modules included the adaptive learning exercise. The exercises and quizzes were assigned to specific textbook modules. To reduce irrelevant variation, all objects of the course covered the same content, had equal learning results, and were taught by the same instructor.

The experimental group—in which students were asked to bring smartphones—was taught, where the how-to adaptive learning application for adaptive learning was downloaded, and a special account was created for each student, followed by access to the channel designed by the through the application, and the students were provided with instructions and training on how entering application with the appropriate default element of the developed learning objects, while the control group used the variety of instructional materials in the same course for the students.

In this adaptive e-course, students in the experimental group are presented with a questionnaire asked to answer that questions via a developed mobile App. They are provided with four choices. Students are allowed to answer the questions. The correct answer is shown in the students' responses to the results, but the learning module is marked as incomplete. If a student chooses to respond to a question, the correct answer is found immediately, regardless of the student's reaction.

Figure  8 illustrates a visual example from learning styles identification through responding VARK Questionnaire. The learning process experienced by the students in this adaptive Learning environment is as shown in Fig.  4 . Students opened the adaptive course link by tapping the following app " https://play.google.com/store/apps/details?id=com.pointability.vark ," which displayed the appropriate positioning of both the learning skills course and the current status of students. It directed students to the learning skills that they are interested in learning more. Once students reached a specific situation in the e-learning environment, they could access relevant digital instructional materials. Students were then able to progress through the various styles offered by the proposed method, giving them greater flexibility in their learning pace.

Visual example from "learning of the learning styles" identification and adaptive e-learning course process

The "flowchart" diagram below illustrates the learner's path in an adaptive e-learning environment, depending on the (VARK) learning styles (visual, auditory, kinesthetic, reading/writing) (Fig. 9 ).

Student learning path

According to the previous design model of the adaptive framework, the students responded "Learning Styles" questionnaire. Based on each student's results, the orientation of students will direct to each of "Visual", "Aural", "Read-Write", and "Kinesthetic". The student took at the beginning the engagement scale online according to their own pace. When ready, they responded "engagement scale".

Based on the results, the system produced an individualized learning plan to fill in the gap based on the VARK questionnaire's first results. The learner model represents important learner characteristics such as personal information, knowledge level, and learning preferences. Pre and post measurements were performed for both experimental and control groups. The experimental group was exposed only to treatment (using the adaptive learning environment).

To address the second question, which states: “What is the impact "effect" of adaptive e-learning based on (VARK) learning styles on development students' engagement (skills, participation/interaction, performance, emotional) in comparison with conventional e-learning?

The validity of the second hypothesis of the research hypothesis was tested, which states " There is a statistically significant difference at the level of (0.05) between the students' mean scores of the experimental group (adaptive e-learning) and the scores of the control group (conventional e-learning) in post-application of students' engagement factors in favor of the experimental group". To test the hypothesis, the arithmetic means, standard deviations, and "T"-test values were calculated for the results of the two research groups in the application of engagement scale factors".

Table 4 . indicates that students in the experimental group had significantly higher mean of engagement post-test (engagement factors items) scores than students in the control group ( p  < 0.05).

The experimental research was performed to evaluate the impact of the proposed adaptive e-learning. Independent sample t-tests were used to measure the previous behavioral engagement of the two groups related to topic of this research. Subsequently, the findings stated that the experimental group students had higher learning achievement than those who were taught using the conventional e-learning approach.

To verify the effect size of the independent variable in terms of the dependent variable, Cohen (d) was used to investigate that adaptive learning can significantly students' engagement. According to Cohen ( 1992 ), ES of 0.20 is small, 0.50 is medium, and 0.80 is high. In the post-test of the student engagement scale, however, the effect size between students' scores in the experimental and control groups was calculated using (d and r) using means and standard deviations. Cohen's d = 0.826, and Effect-size r = 0.401, according to the findings. The ES of 0.824 means that the treated group's mean is in the 79th percentile of the control group (Large effect). Effect sizes can also be described as the average percentile rank of the average treated learner compared to the average untreated learner in general. The mean of the treated group is at the 50th percentile of the untreated group, indicating an ES of 0.0. The mean of the treated group is at the 79th percentile of the untreated group, with an ES of 0.8. The results showed that the dependent variable was strongly influenced in the four behavioral engagement factors: skills: performance, participation/interaction, and emotional, based on the fact that effect size is a significant factor in determining the research's strength.

Discussions and limitations

This section discusses the impact of an adaptive e-learning environment on student engagement development. This paper aimed to design an adaptive e-learning environment based on learning style parameters. The findings revealed that factors correlated to student engagement in e-learning: skills, participation/interaction, performance, and emotional. The engagement factors are significant because they affect learning outcomes (Nkomo et al., 2021 ). Every factor's items correlate to cognitive process-related activities. The participation/interaction factor, for example, referred to, interactions with the content, peers, and instructors. As a result, student engagement in e-learning can be predicted by interactions with content, peers, and instructors. The results are in line with previous research, which found that customized learning materials are important for increasing students' engagement. Adaptive e-learning based on learning styles sets a strong emphasis on behavioral engagement, in which students manage their learning while actively participating in online classes to adapt instruction according to each learning style. This leads to improved learning outcomes (Al-Chalabi & Hussein, 2020 ; Chun-Hui et al., 2017 ; Hussein & Al-Chalabi, 2020 ; Pashler et al., 2008 ). The experimental findings of this research showed that students who learned through adaptive eLearning based on learning styles learned more; as learning styles are reflected in this research as one of the generally assumed concerns as a reference for adapting e-content path. Students in the experimental group reported that the adaptive eLearning environment was very interesting and able to attract their attention. Those students also indicated that the adaptive eLearning environment was particularly useful because it provided opportunities for them to recall the learning content, thus enhancing their overall learning impression. This may explain why students in the experimental group performed well in class and showed more enthusiasm than students in the control group. This research compared an adaptive e-learning environment to a conventional e-learning approach toward engagement in a learning skills course through instructional content delivery and assessment. It can also be noticed that the experimental group had higher participation than the control group, indicating that BB activities were better adapted to the students' learning styles. Previous studies have agreed on the effectiveness of adaptive learning; it provides students with quality opportunity that is adapted to their learning styles, and preferences (Alshammari, 2016 ; Hussein & Al-Chalabi, 2020 ; Roy & Roy, 2011 ; Surjono, 2014 ). However, it should be noted that this study is restricted to one aspect of content adaptation and its factors, which is learning materials adapting based on learning styles. Other considerations include content-dependent adaptation. These findings are consistent with other studies, such as (Alshammari & Qtaish, 2019 ; Chun-Hui et al., 2017 ), which have revealed the effectiveness of the adaptive e-learning environment. This research differs from others in that it reflects on the Umm Al-Qura University as a case study, VARK Learning styles selection, engagement factors, and the closed learning management framework (BB).

The findings of the study revealed that adaptive content has a positive impact on adaptive individuals' achievement and student engagement, based on their learning styles (kinesthetic; auditory; visual; read/write). Several factors have contributed to this: The design of adaptive e-content for learning skills depended on introducing an ideal learning environment for learners, and providing support for learning adaptation according to the learning style, encouraging them to learn directly, achieving knowledge building, and be enjoyable in the learning process. Ali et al. ( 2019 ) confirmed that, indicating that education is adapted according to each individual's learning style, needs, and characteristics. Adaptive e-content design that allows different learners to think about knowledge by presenting information and skills in a logical sequence based on the adaptive e-learning framework, taking into account its capabilities as well as the diversity of its sources across the web, and these are consistent with the findings of (Alshammari & Qtaish, 2019 ).

Accordingly, the previous results are due to the following: good design of the adaptive e-learning environment in light of the learning style and educational preferences according to its instructional design (ID) standards, and the provision of adaptive content that suits the learners' needs, characteristics, and learning style, in addition to the diversity of course content elements (texts, static images, animations, and video), variety of tests and activities, diversity of methods of reinforcement, return and support from the instructor and peers according to the learning style, as well as it allows ease of use, contains multiple and varied learning sources, and allows referring to the same point when leaving the environment.

Several studies have shown that using adaptive eLearning technologies allows students to improve their learning knowledge and further enhance their engagement in issues such as "skills, performance, interaction, and emotional" (Ali et al., 2019 ; Graf & Kinshuk, 2007 ; Murray & Pérez, 2015 ); nevertheless, Murray and Pérez ( 2015 ) revealed that adaptive learning environments have a limited impact on learning outcome.

The restricted empirical findings on the efficacy of adapting teaching to learning style are mixed. (Chun-Hui et al., 2017 ) demonstrated that adaptive eLearning technologies can be beneficial to students' learning and development. According to these findings, adaptive eLearning can be considered a valuable method for learning because it can attract students' attention and promote their participation in educational activities. (Ali et al., 2019 ); however, only a few recent studies have focused on how adaptive eLearning based on learning styles fits in diverse cultural programs. (Benhamdi et al., 2017 ; Pashler et al., 2008 ).

The experimental results revealed that the proposed environment significantly increased students' learning achievements as compared to the conventional e-learning classroom (without adaptive technology). This means that the proposed environment's adaptation could increase students' engagement in the learning process. There is also evidence that an adaptive environment positively impacts other aspects of quality such as student engagement (Murray & Pérez, 2015 ).

Conclusions and implications

Although this field of research has stimulated many interests in recent years, there are still some unanswered questions. Some research gaps are established and filled in this study by developing an active adaptive e-learning environment that has been shown to increase student engagement. This study aimed to design an adaptive e-learning environment for performing interactive learning activities in a learning skills course. The main findings of this study revealed a significant difference in learning outcomes as well as positive results for adaptive e-learning students, indicating that it may be a helpful learning method for higher education. It also contributed to the current adaptive e-learning literature. The findings revealed that adaptive e-learning based on learning styles could help students stay engaged. Consequently, adaptive e-learning based on learning styles increased student engagement significantly. According to research, each student's learning style is unique, and they prefer to use different types of instructional materials and activities. Furthermore, students' preferences have an impact on the effectiveness of learning. As a result, the most effective learning environment should adjust its output to the needs of the students. The development of high-quality instructional materials and activities that are adapted to students' learning styles will help them participate and be more motivated. In conclusion, learning styles are a good starting point for creating instructional materials based on learning theories.

This study's results have important educational implications for future studies on the effect of adaptive e-learning on student interaction. First, the findings may provide data to support the development and improvement of adaptive environments used in blended learning. Second, the results emphasize the need for more quasi-experimental and descriptive research to better understand the benefits and challenges of incorporating adaptive e-learning in higher education institutions. Third, the results of this study indicate that using an adaptive model in an adaptive e-learning environment will encourage, motivate, engage, and activate students' active learning, as well as facilitate their knowledge construction, rather than simply taking in information passively. Fourth, new research is needed to design effective environments in which adaptive learning can be used in higher education institutions to increase academic performance and motivation in the learning process. Finally, the study shows that adaptive e-learning allows students to learn individually, which improves their learning and knowledge of course content, such as increasing their knowledge of learning skills course topics beyond what they can learn in a conventional e-learning classroom.

Contribution to research

The study is intended to provide empirical evidence of adaptive e-learning on student engagement factors. This research, on the other hand, has practical implications for higher education stakeholders, as it is intended to provide university faculty members with learning approaches that will improve student engagement. It is also expected to offer faculty a framework for designing personalized learning environments based on learning styles in various learning situations and designing more adaptive e-learning environments.

Research implication

Students with their preferred learning styles are more likely to enjoy learning if they are provided with a variety of instructional materials such as references, interactive media, videos, podcasts, storytelling, simulation, animation, problem-solving, games, and accessible educational tools in an e-learning environment. Also, different learning strategies can be accommodated. Other researchers would be able to conduct future studies on the use of the "adaptive e-learning" approach throughout the instructional process, at different phases of learning, and in various e-courses as a result of the current study. Meanwhile, the proposed environment's positive impact on student engagement gained considerable interest for future educational applications. Further research on learning styles in different university colleges could contribute to a foundation for designing adaptive e-courses based on students' learning styles and directing more future research on learning styles.

Implications for practice or policy:

Adaptive e-learning focused on learning styles would help students become more engaged.

Proving the efficacy of an adaptive e-learning environment via comparison with conventional e-learning .

Availability of data and materials

The author confirms that the data supporting the findings of this study are based on the research tools which were prepared and explained by the author and available on the links stated in the research instruments sub-section. The data analysis that supports the findings of this study is available on request from the corresponding author.

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Acknowledgements

The author would like to thank the Deanship of Scientific Research at Umm Al-Qura University for the continuous support. This work was supported financially by the Deanship of Scientific Research at Umm Al-Qura University to Dr.: Hassan Abd El-Aziz El-Sabagh. (Grant Code: 18-EDU-1-01-0001).

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Hassan A. El-Sabagh is an assistant professor in the E-Learning Deanship and head of the Instructional Programs Department, Umm Al-Qura University, Saudi Arabia, where he has worked since 2012. He has extensive experience in the field of e-learning and educational technologies, having served primarily at the Educational Technology Department of the Faculty of Specific Education, Mansoura University, Egypt since 1997. In 2011, he earned a Ph.D. in Educational Technology from Dresden University of Technology, Germany. He has over 14 papers published in international journals/conference proceedings, as well as serving as a peer reviewer in several international journals. His current research interests include eLearning Environments Design, Online Learning; LMS-based Interactive Tools, Augmented Reality, Design Personalized & Adaptive Learning Environments, and Digital Education, Quality & Online Courses Design, and Security issues of eLearning Environments. (E-mail: [email protected]; [email protected]).

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El-Sabagh, H.A. Adaptive e-learning environment based on learning styles and its impact on development students' engagement. Int J Educ Technol High Educ 18 , 53 (2021). https://doi.org/10.1186/s41239-021-00289-4

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Gamification Applications in E-learning: A Literature Review

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In recent years, there has been a lot of attention given to the trend of including game elements into non-gaming facilities. The usage of gamification in education is a massive benefit for motivation, user interaction, and social effects. The gamified elements such as points, badge, feedbacks, level, rewards, challenges, etc. have been used in e-learning. A systematic review of gamification in online education has not been found when the relevant literature examined. Therefore, this study aims to research the current literature using gamification and online education and highlight the reported benefits and challenges of gamification applications in online education. The present research followed the literature review method. The current study employed a qualitative approach for collected data. Thus, the term "gamification" was used as the primary research keyword. The results show that gamification has increasingly been accepted as a useful learning tool to generate more engaging educational environments. Additionally, elements support and motivate students to participate in a gamification system. The study showed that the most common gamification elements used in e-learning and have a powerful effect on the students are points, leaderboards, badge, and level. This study is thought to contribute significantly to studies on the use of gamification applications in online education. It reinforces previous studies and identifies many useful study topics that can be explored to advance the field. From these results, suggestions on gamification applications in e-learning for further research are given.

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Saleem, A.N., Noori, N.M. & Ozdamli, F. Gamification Applications in E-learning: A Literature Review. Tech Know Learn 27 , 139–159 (2022). https://doi.org/10.1007/s10758-020-09487-x

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Systematic research of e-learning platforms for solving challenges faced by Indian engineering students

Asian Association of Open Universities Journal

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Article publication date: 2 December 2020

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As educational institutes began to address the challenges posed by COVID-19, e-learning came to the foreground as the best bet left. This study is in quest of revealing engineering student's perceptions of the available e-learning platforms, thus surfacing the underlying bottlenecks. Further, it aims at providing solutions that would help enhance the e-learning experience not only in pandemic times but also in the long run.

Design/methodology/approach

This holistic research begins with a comprehensive comparative study about the available e-learning platforms, followed by a primary data analysis through an online survey of 364 engineering students from various colleges and branches. The collected data was analyzed to detect bottlenecks in online learning and suggestions are given for solving some challenges.

On a five-point Likert scale, the available e-learning platforms garnered ratings ranging from 2.81 to 3.46. Google meet was the most preferred platform. However, with a net promoter score (NPS) of 30.36, Microsoft Teams emerged as the most satisfying platform. Technical shortcomings clubbed with psychological and biological factors were found to be taking a toll on e-learning.

Research limitations/implications

This innovative research is based on the perceptions of engineering students hailing majorly from Indian cities, and hence, it may be having educational stream bias and geographical bias. The research could be further extended to cover rural areas and global trends in e-learning.

Originality/value

The research offers a thorough analysis of e-learning platforms, as seen through the lens of engineering students. Furthermore, the analysis does not constrain itself to the technicalities and thus proves to be an all-encompassing one, potent enough to surface critical issues marring the e-learning experience.

• Online teaching
• E-learning platform
• Engineering education
• Open and Distance education

Thakker, S.V. , Parab, J. and Kaisare, S. (2021), "Systematic research of e-learning platforms for solving challenges faced by Indian engineering students", Asian Association of Open Universities Journal , Vol. 16 No. 1, pp. 1-19. https://doi.org/10.1108/AAOUJ-09-2020-0078

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Copyright © 2020, Shivangi Viral Thakker, Jayesh Parab and Shubhankar Kaisare

Published in the Asian Association of Open Universities Journal . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Acting as an interface between science and society, engineering surpasses the boundaries of knowledge, analysis and practices the sublime art of organizing forces of technological change. An effective transfer of engineering education stands on the pillars of remembering, understanding, applying, analyzing, evaluating and creating ( Barak, 2013 ).

With the advent of neoliberal market policies, the private sector in engineering and technical education has shifted the focus from philanthropy to profit thereby resulting in a poor quality of education and a mere 25% employability rate ( Choudhury, 2019 ; Gambhir et al. , 2016 ). To add to the existing troubles, COVID-19 has caused education to blow up in the air like an unprecedented display of fireworks. Education systems have no choice but to accept the digital checkmate imposed, ranging from major shutdowns of classroom teaching to spontaneous virtualization ( Xiao, 2018 ). Lack of access to remote learning tools and materials has pushed an alarming number of students not just out of colleges but also out of the system ( Azorín, 2020 ).

COVID-19 has left the post-pandemic education system with two possibilities: a return to traditional education or a transformation towards enhanced education. The key to transformational change will be for systems to focus on their professional capital and find ways to develop teachers' knowledge and skills, support effective collaborative networks that include parents ( McPhee and Söderström, 2012 ). Including educators in the decision- making and communication process ensures easy transformation ( Hollweck and Doucet, 2020 ).

This paper identifies the current perception of available e-learning platforms among engineering students. A comprehensive internal study followed by a thorough analysis helps detect the underlying problems. Further, the paper proposes solutions for these problems to ensure that the transition to e-learning is a smooth endeavor.

2. Literature review

There are recent studies on the demands and needs of engineering education and the exact process of distance learning in the Internet environment. The current effect of the COVID-19 pandemic on the education sector globally and countrywide is studied by few researchers recently. There is a dire need to search for online learning tools available currently and their impact on future e-learning and classroom learning aspects ( Hillier, 2018 ). The following sub-sections give a detailed literature review done on Engineering education requirements, the effect of the pandemic on the education system, various e-learning platforms, and a comparison of online survey methods.

2.1 Engineering education

Fuentes-Del-Burgo and Navarro-Astor (2016) explained in brief how the concepts of “episteme,” “techne” and “phronesis” given by Aristotle are associated with engineering education. Though mainly concerned with Spanish building engineers, it almost gives a worldwide perspective on how various educational factors play an important role in building good engineers and various suggestions to improve them. Barak (2013) discusses how the United States has implemented the three principles of K-12 education and how it can be utilized in other countries to have an overall development of engineering students. The difference between technology and engineering; integration of Bloom's Taxonomy and K-12 education; and the importance of cognitive education on the improvement of engineering students have also been explained.

Choudhury (2019) , surveyed 1178 undergraduate engineering students in Delhi to understand student's perceptions of various criteria of teaching methods used, skills acquired by the students, and involvement of students. This does provide a brief overview of the current situation of engineering education in India and how the current teaching methods can be improved. Upadhayay and Vrat (2017) , have analyzed the growth of India's technical education from the system's dynamic perspective followed by a comparison with the Gartner Hype cycle. The concept of the Boomerang effect has been introduced in this paper to compare it with the current movement of Indian technical education along with concerns over the quality of technical education currently in India. Gambhir et al. (2016) echo the same concerns and have developed a methodology to build a quality model for the integration of various factors in a technical institute.

2.2 COVID-19 pandemic's effect on education

UNESCO report (2020) gives some interesting numbers on how much the pandemic has affected the education system globally. The study indicates that 60.9% of the enrolled student population has been affected with over 1 billion student learners affected and 107 nationwide closures (as of July 2020). The study shows that this pandemic has crippled the education system all over the world further emphasizing the need for new measures to be taken to handle this inopportune time.

Carter et al. (2020) have discussed the effect of COVID-19 on classroom education and how e-learning would need to come to the forefront. The concept of self-regulated learning has been introduced along with its components and ways to integrate it with online learning. Hollweck and Doucet (2020) have also discussed the effects of COVID-19 on education, but they have created an interesting analogy with supernova. They have compared COVID-19 with a supernova in terms that after a supernova event everything changes for the better and the status quo are broken which was unraveling before. Similarly, the paper takes this pandemic as an opportunity to disrupt the status quo and build the education system in a much better way.

Further, Fullan (2020) reiterates that evolution could have wonderful things in store for us - but only if we do our part to shape it and thus hints to take this pandemic positively. Izumi et al. (2020) have similarly gone about discussing the issue of COVID-19 on the transition from classroom learning to online learning and the financial strains of the same. They have surveyed to understand the preparedness for such a transition and the available infrastructure. This has given great insight into the current capacity of the world to transform itself from classroom teaching to online learning.

Lall and Singh (2020) have discussed the impact of COVID-19 on India, emphasizing the importance of a smooth transition towards online learning. A survey to gauge the current perception towards online learning, drawbacks of it, and also the preferred mode of learning was done, which gives a great insight into what factors affect the success of online learning. Similar survey-based research has been done by Basilaia and Kvavadze (2020) with great emphasis on the transition to online learning in Georgia. A brief discussion on the social impact of this immediate transition from classroom learning to online learning has been done by Kufi et al. (2020) along with the importance of free online courses and how resource use should be done in schools to tackle this situation.

2.3 E-learning

Harper et al. (2004) explain distance learning, the advancement of the same along with the impact of government involvement on distance learning. The authors embellish the current details with information on the role of participants in the success of distance learning, change in the organizational structure required for the success of distance learning, and the pros and cons of it in long term perspectives. Along similar lines, Au et al. (2018) discuss the success factors for students learning online such as time management, online examination conduction and flexibility. Jones et al. (2014) discuss how the introduction of technology affects the temporal experience of the learner and states the importance of time flexibility which must be introduced in online learning. This, in a direct sense, gives an understanding of how synchronous and asynchronous ways of teaching can affect the learning capacity of a student. Though Fang et al. (2019) discuss the evolution of MOOCs from 2009 to 2018 in language learning through literature study, they also reiterate similar points and their results show that online learning courses and platforms have raised the time and space for learning, which has made it flexible.

Veletsianos and Houlden (2019) have discussed various themes associated with distance learning. These themes have been closely associated with flexibility and further discuss various approaches towards it from a pedagogical, liberal, temporal and cultural point of view for the past 40 years of distance learning. A similar analysis of the flexibility of transactional education has been done by Paul et al. (2015) . Even Naidu (2019) has given a brief overview of how open learning, flexible learning, and e-learning are very dynamic with narrative changing at any given point of time. Its psychological impact on students also has been reviewed along with various advantages and disadvantages of it. A similar yet a very unique study also has been done by Estacio and Raga (2017) , where they have used machine learning models and correlated the quantitative data available from Moodle to the online learning behavior of students, where the grades obtained are used as a determining medium.

Major et al. (2014) explained the various pedagogical approaches which can be used in distance learning like constructivist, problem-based learning, holistic approach, teamwork. They further provided an overview of how the transition to the online setting must be done along with the technological challenges associated with it. Joanna Rabiega-Wiśniewska (2020) conducted a case study on the current perception of e-learning at Maria Grzegorzewska University, Poland. The study does indicate a neutral stance over liking of the immediate change in learning method, but with 91% of students having a stable Internet connection; it's a good sign nonetheless. A brief understanding of the type of scaling system to be used in such surveys has been explained, which shall be imbibed in this paper to enhance and avoid response bias and to evaluate NPS. Marengo and Marengo (2005) have discussed in brief the actual organizational structure and proper education requirements through Kirkpatrick's taxonomy which needs to be imbibed in e-learning. The concept of blended learning also has been introduced in this paper with its pros and cons in economic terms. The study has effectively discussed various costs involved in e-learning along with the benefits gained. These costs have played an important role in deciding parametric questions to be asked to the students for correct evaluation of the current perception of e-learning tools among engineering students.

2.3.1 Comparison of online platforms for e-learning

A comprehensive comparative study becomes crucial to determine the publicly known best available tools as floating a survey on the unpopular tools may hamper the survey outcome significantly. To aid this comparison, identification of parameters to be compared must be identified. Agrawal et al. (2016) discuss in brief how a parametric survey needs to be conducted and the importance of information quality, service quality, system quality in the success of e-learning. Further, James-Gordon et al. (2003) explain the importance of security features required for e-learning to not be a hindrance for people and an understanding of how market demand or the popularity of a learning platform affects its overall success. Wong (2015) mentions the importance of flexibility of the platforms provided for MOOCs and this flexibility will play a big role in these e-learning platforms as well. Keeping these factors in mind, various parameters such as features provided, platforms which the tool supports, typical customers the tool attracts, customer support provision, price of the tool, overall customer perception about the tool, third party integration, the scope of the tool have been devised for comparison.

2.3.2 Comparison of survey methods

Surveys can be conducted in two ways: online and offline. Offline surveys are generally avoided as they have a localized outreach and getting timely responses is a big task. Online surveys break the barriers of distance and have a hassle-free response collection process. Online survey forms have an easy build coupled with cost-effectiveness. There are various online survey platforms available and a proper comparison must be done among them to find out an apt option for the survey.

To accurately garner student perceptions, the online survey tool should be selected with keen consideration. Along with cost-effectiveness, this tool should bring the magical combination of accuracy and customization. To narrow down on the best survey tool, it was necessary to adopt a comprehensive approach that compared these tools based on the parameters like permissible number of questions, permissible number of responses, data export availability and options, number of free surveys allowed, customization and its scale. Table 1 displays the permissible values with the free version of the tool along with the cost to upgrade to the premium version.

3. Methodology

3.1 data collection tool.

A comprehensive study of seven e-learning platforms (Zoom, Google Meet, Microsoft Teams, GoToWebinar, Zoho Meeting, Adobe Connect and GoToMeeting) was performed to gauge the consistency and performance of platforms based on features, security, customer support and third-party integrations. This study acted as predictive analysis to understand what could be the student's standpoint and then understand how much it varies. Further, a comparative analysis was adopted to find the most suitable online survey platform. A survey-based approach was adopted to gauge the perception of engineering students on the available e-learning tools. Through the review done above, Google Forms was finalized as the survey platform.

3.2 Questionnaire for survey

For drawing valuable insights, it is vital to analyze the most critical parameters. A respondent friendly survey was constructed on Google Forms wherein the respondents had to rate the e-learning platforms based on the parameters like video quality, audio quality, privacy/security, multi-device support, user-friendliness of the interface, screen sharing, chat features, host's control and quality of meeting recording. Figure 1 shows the flow of the questionnaire.

The questionnaire ratings were taken on a five-point Likert scale developed by Rensis Likert ( Reichheld, 2003 ) as this type of scale is used in attitude research projects ( Joanna Rabiega-Wiśniewska, 2020 ). An odd-numbered Likert scale was used to avoid emotion bias and to provide an option for indecision, negativity, and positivity ( Croasmunand Ostrom, 2011 ).

3.3 Distribution channel

A robust distribution channel ensures a greater number of responses from students, spread across various engineering colleges and branches. To achieve the same, the survey form was circulated through platforms like WhatsApp, Gmail, Instagram, LinkedIn and personal calling.

These tools and methods helped in collecting responses from students spread across 12 branches and 49 colleges. The responses generated from surveys generally depict a bell curve. In such cases, if the sample size or the number of respondents is very large, the confidence interval narrows down and errors decrease. Error reduction is good, but the confidence interval should not decrease to a point where it starts showing that negligible people have positive responses. Now, with a decrease in sample size, the confidence interval increases but the error also increases. Thus, selecting the number of respondents is a double-edged sword as a perfect balance has to be struck among confidence interval and error. Hence, an optimal range of 350–400 responses was chosen and the survey form was closed on receiving 364 responses.

4. Data analysis

4.1 respondents profiles.

A total of 364 responses were collected from 49 colleges across India. It was ensured that all the respondents have extensively used the platforms voted by them for at least a month. This data needs to be sorted into various categories to identify trends and gain insights from them. These responses were analyzed branch–wise and year-wise to check whether there is slight response bias, to identify trends, and to draw insights based on the same (see Figures 2 and 3 ).

The Mechanical branch accounted for 44.23% of responses and had the maximum number of responses. Computer Science and Engineering (CSE) branch was second to the Mechanical branch and held 23.07% of responses. Information Technology (IT) branch and Electronics and Telecommunication branch (EXTC) had an almost similar number of respondents and contributed 9.89 and 9.07% of responses respectively. This does indicate that the perception generated was slightly biased towards the requirements of Mechanical Engineering students, but on a closer look at the data, the platforms selected and the ratings given by other branches were on similar lines as the Mechanical branch. Last year students of engineering submitted the maximum responses indicating that maximum awareness, for now, has been limited to certain students only with the further scope for improvement. As first-year students had just been admitted to their respective colleges when the survey was conducted, they were not exposed to the e-learning environment thus resulting in a fewer number of responses from the first year.

4.2 Net promoter score of platforms

The data, collected from the survey responses of 364 students, was analyzed firstly by segregating and making a college wise distribution of responses to check the demographic reach of the survey. A wider demographic reach ensures a varied perspective thereby eliminating regional bias. Branch wise distribution of responses was also plotted to check for singular branch bias for a particular online learning tool. A similar approach was used to check singular year bias by plotting the year-wise distribution of responses. This data was crucial in understanding how the perception is influenced by branch and year of study.

Awareness of platforms was analyzed to check the popularity or reach of each platform irrespective of its liking or disliking. The average ratings of each platform based on the nine parameterized survey outcomes provided insights as to which platform has been consistent in providing all the features satisfactorily to its target audience.

Further, an NPS for each online learning platform was evaluated. NPS is a loyalty index introduced by Frederick F. Reichheld in 2003, primarily used to evaluate how much a product has been liked by the customers and can be used for further product referrals. Promoters are individuals who strongly recommend the product and are convinced of the parameter, thus rating it 4 or 5. Detractors are individuals who are unsatisfied with the product or some parameter of it, thus rating it 1 or 2. Individuals, who give a rating of 3, lie between these two categories and are called passives. NPS for a particular platform, on a 5-point Likert scale, is evaluated as: NPS = ( Number   of    promoters − Number   of   detractors )  *  100 Number   of    respondents   who    have   used   that   platform

(−100 to 0): Needs improvement

(0–30): Good

(30–70): Very good

(70–100): Excellent

The above ranges helped to boil down the overall user sentiment into a single quantifiable value and classify the platform on the same. Finally, the preference percentage was plotted for each platform to understand the current perception and to recognize which platform currently is ruling the roost in the online learning world among engineering students.

5. Results and interpretations

5.1 internal study outcomes.

The internal study focused on performing a comparative analysis of the available e-learning platforms. By comparing these platforms based on the offered features, integrations, reviews, and pricing, the study aimed at finding a platform that provided a complete package to its users at a reasonable subscription cost. Table 2 provides an overview of the internal study outcome.

From Table 2 , it is evident that Zoom and Microsoft Teams are the best platforms with 44 and 67 features available respectively. A closer introspection does reveal a shortcoming of Microsoft Teams over Zoom that is the absence of an attendance management system. In terms of security aspects, Google Meet, GoToMeeting, and GoToWebinar do not have access control and an activity dashboard thereby making these platforms weak. The only salvation for Google Meet is that it has a better API. A bird's eye view indicates that Google Meets supports all the platforms available to people, whereas Zoom and Microsoft Teams do not support the Windows phone app. Microsoft Teams does not attract freelancers and does not provide customer support over the phone. Other platforms satisfactorily provide this, thereby leaving Microsoft Teams with a massive scope of improvement in this aspect. Google Meet is the best in this aspect followed closely by Zoom.

As visible from both Table 3 , Microsoft Teams is the most feasible platform whereas GoToWebinar is the highest priced platform. Zoom and Google Meet are also priced affordably but Microsoft Teams wins the battle in pricing. Table 4 shows that the rankings of all the platforms are not too bad, all crossing 4 stars, but the number of reviews given for Zoom and Google Meet shows that they are the most popular platforms among the others. Zoho Meeting though not as popular, has been highly ranked by those who have used it. Zoom and Google Meet are closely followed by Microsoft Teams which ranks third in popularity. The pricing of GoToWebinar and Adobe Connect does surely reflect their lack of popularity amongst general people. Table 4 does show that Zoom is the platform with the highest number of Third-Party Integrations amounting to whopping 170 integrations. It is closely followed by Microsoft Teams with 154 Third-Party integrations. Other platforms need improvements in this aspect with 86 integrations from GoToWebinar and then an equally shocking drop to 15 integrations from Google Meet. This does show that Third-Party Integrations are surely a challenge for these platforms, Zoom and Microsoft Teams being the only exceptions.

These results show that Zoom has the best balance among features, overview, pricing, popularity, third-party integrations as compared to other platforms. Though just by score value, Microsoft Teams should have followed as the next best; the graph shows high inconsistencies in these parameters. This indicates that due notice over certain parameters has not been given in Microsoft Teams. This makes Google Meet slightly more favorable over Microsoft Teams. Adobe Connect and Zoho Meeting do not make a case to prove their chance in the education sector with even GoToWebinar becoming a rare case of use due to its high price (see Figures 4–6 ).

5.2 Awareness of platforms

Zoom and Google Meets are the most publicly known platforms with an astounding awareness percentage of 86 and 81.6% respectively. Adobe connect and Zoho Meeting is the least known ones and the perception matches the internal study where the higher pricing and fewer features value seen in the graph of these tools had made them possibly least known ones. Thus, there are increased chances of Google Meets and Zoom ruling the roost in the online education industry as these are the platforms mostly used (see Table 5 ).

5.3 Comparison of platforms based on survey results

These ratings indicate that Microsoft Teams is the best platform followed by Google Meets, Zoom, GoToWebinar, GoToMeeting, Zoho Meeting and Adobe Connect. Microsoft Teams had a maximum rating of 3.46 closely followed by Google Meets with a rating of 3.45. No platform had an average rating beyond 4. These passive ratings indicate an even greater perspective over the audience being a low tolerant one with a keen eye towards perfection. Considering this it can be predicted that the NPS would not be very high and in a rare case, it would breach the barrier of 30 (see Table 6 ).

5.4 Net promoter score

Microsoft Teams has the maximum NPS of 30.36, overcoming the Good band and entering the Very Good band. Adobe Connect has the worst NPS of −50 indicating that it needs improvement. Also, the NPS of almost all platforms is closely ranged showing that the competition is very stiff (see Figures 7 and 8 ).

5.5 Preference of platforms

A majority of the audience has given preference to Google Meets followed by Zoom and Microsoft Teams respectively. Here, though the quality of Microsoft Teams is much higher than that of both Zoom and Google Meets, lack of awareness of Microsoft Teams has resulted in it being ruled out of favor. There is also a curious case of Zoom, but a closer introspection shows that concerns over privacy and security of the platform have caused it to be not as favorable as Google Meet, though being superior in other features. Zoho Meetings and Adobe Connect have not made it through as far as students' preference is concerned.

6. Challenges and solutions

The survey respondents highlighted several shortcomings which were barring them from having an effective e-learning experience. Along with these shortcomings, the respondents expressed their desire for certain additional features which would greatly boost the e-learning experience.

6.1 Security concerns

A high number of students are attending digital classrooms and it has become easier for cybercriminals to hijack meetings. Events of video hijacking by uninvited parties to disrupt the usual proceedings have been on the rise since the global quarantine began. Spreading hateful comments, racist and obscene content on these platforms has given rise to a new kind of Internet trolling. Further, unwarranted logins to the enterprise cloud architecture have resulted in immense data breaches.

To greatly reduce such malpractices, the responsibility lies on the shoulders of the platform, the host, and the attendees. Platforms have been striving to enhance their security measures and have also created robust privacy policies. Hosts should secure meetings with a passcode and use private distribution channels to invite participants. Also, disabling features like join before host and participant screen sharing would provide a greater immunity against hijacking. Attendees should refrain from sharing the meeting details on public platforms and avoid clicking on any malicious links.

6.2 Online engagement concerns

6.2.1 proctor mode.

After spending huge amounts on these e-learning platforms, educational institutions do not prefer using separate applications designed specifically for proctoring. This leaves them with two broad options which are to either conduct examinations without proctoring or to use the same e-learning platform for proctoring. The former invites a large number of unfair practices and thus is unjust for diligent students ( Nguyen, 2015 ). The latter requires all the participants to switch on their video which consumes a great amount of bandwidth resulting in lags. Even if incoming videos are disabled, the bandwidth problem persists with the host which leads to difficulties in proctoring ( Gillett-Swan, 2017 ; Dhawan, 2020 ). Thus, the introduction of a specialized proctor mode on these platforms is a desire of many students.

6.2.2 Lecture mode

Survey respondents reported that mischiefs by certain students (e.g.: disturbing annotations on the screen, muting the instructor, etc.) disrupt the flow of the lectures. Though the platforms have provided certain host-specific features, the spontaneous virtualization of education resulted in the instructors getting insufficient time to adapt themselves to these features. This issue has also been highlighted by Moradimokhles and Hwang (2020) as a limitation of online learning. As a result, a majority of them are not aware of or are unable to use all the features they have at their disposal. Even before the pandemic hit, this adaptability was an issue that was highlighted by Parkes et al. , (2014) . As a result, a majority of them are not aware of or are unable to use all the features they have at their disposal. Bringing all these features under a single button of lecture mode would thus help in conducting lectures smoothly without mischiefs.

6.3 Introduction of new features

In addition to the existing features, the respondents expressed the need for certain features. A large number of instructors annotate the content to provide a lucid explanation. However, the students can download the file without any annotations. An option to download it with annotations would ensure a quicker grasping of the concept when students revisit that concept. An inbuilt notepad that can be opened along with the lecture content, in a split-screen mode, would make the notes taking process hassle-free. Live polling would facilitate the instructor in a variety of ways. Similarly, live quizzes with leader-boards would not only add an element of fun to the learning but would also be an indicator of how much the students have learned ( Huang et al. (2019a , b) , Seaborn and Fels (2015) ). The platforms should further be compatible with augmented reality and virtual reality as these would greatly increase the level of understanding ( Bower, 2017 ; El Kabtane et al. , 2019 ; Uhomoibhi et al. , 2019 ). The presence of a virtual user guide along with a chat box would help in resolving the basic issues faced by a great number of users. The ability to rewind live lectures, like YouTube Live, would help students who have missed out on certain important parts of a lecture.

7. Conclusion

Almost all the platforms are sufficient for learning for the time being but have shortcomings that need to be improved to adapt to this fast-changing education sector. There is a large amount of concern over the video and audio quality of all the platforms and the students feel that the platforms are not updated as per current requirements. As per this research study, Google Meet is the best platform among students followed by Zoom and Microsoft Teams respectively, even though NPS indicates Microsoft Teams is the best. If Microsoft teams can improve its social presence, it can prove to be a strong competitor for both Zoom and Google Meet.

The available online learning tools are not the best means to study a holistic curriculum of theory and practical combined. These tools will have to be more adaptable, more technically friendly for the audience to achieve high effectiveness. Along with online learning, methods to improve motivation to study through these means need to be developed ( Roberts et al. , 2018 ). Available e-learning tools serve the basic purpose but integrations of these platforms with other platforms must be improved to give a wider, more enriching experience. Keeping these points in mind, it would not be wrong to conclude that currently, online learning is the best bet left to counter this unprecedented situation in India, but infrastructure development for such platforms needs to be enhanced to consider this method of learning completely fruitful.

8. Limitations and future scope

This research is based on the perceptions of engineering students hailing majorly from Indian cities and is thus subject to educational stream bias and geographical bias. Curbing the educational stream bias by incorporating respondents from other streams could help in understanding the shortcomings on a broader level. Expanding the respondent base by breaking the geographical barriers would help in further understanding the overall access to technology and its implications on the e-learning experience. Furthermore, the overall experience, perceptions, and awareness of students about these platforms are subject to the instructor's ICT proficiency along with the availability and compatibility with the existing infrastructure in the institutions. Considering the unequal penetration of technology across the various socioeconomic classes, an equal amount of focus should be laid on bridging these gaps ( Zhao, 2016 ). As the COVID-19 pandemic is heralding the end of a largely obsolete educational system, developing solutions on a global level while keeping in mind the issues on local levels would bolster the possibility of redesigning a better education system on the bedrocks of equity, excellence and student well-being.

Questionnaire flow chart

Respondents' profiles

Overall aspect comparison of platforms (Individual platform level comparison is given in Appendix )

Platform awareness among respondents

Average survey ratings (For individual platform level comparison, see Appendix )

Platform-based average survey ratings

Comparison of platforms based on NPS

Survey respondents' preference for platforms

Comparison of survey platforms based on features

Overall comparison of platforms

Feature-based comparison of platforms

Overall survey ratings

The appendix file are available online for this article.

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Acknowledgements

The authors are grateful to all the students of various engineering colleges for responding to the survey and using the online platforms for a month as a prerequisite for the survey.

Corresponding author

Supplementary materials.

AAOUJ-09-2020-0078_suppl2.docx (44 KB) AAOUJ-09-2020-0078_suppl1.docx (42 KB) AAOUJ-09-2020-0078_suppl3.docx (27 KB)

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