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E-Learning Research Trends in Higher Education in Light of COVID-19: A Bibliometric Analysis.

作者信息

Brika Said Khalfa Mokhtar, Chergui Khalil, Algamdi Abdelmageed, Musa Adam Ahmed, Zouaghi Rabia

机构信息

University of Bisha, Bisha, Saudi Arabia.

University of Oum El Bouaghi, Oum El Bouaghi, Algeria.

出版信息

Front Psychol. 2022 Mar 3;12:762819. doi: 10.3389/fpsyg.2021.762819. eCollection 2021.


DOI:10.3389/fpsyg.2021.762819
PMID:35308075
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8929398/
Abstract

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.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/26ef51aa7932/fpsyg-12-762819-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/a059f7d3b29e/fpsyg-12-762819-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/5d469868e052/fpsyg-12-762819-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/dc64f2a00a24/fpsyg-12-762819-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/604cb9d13da6/fpsyg-12-762819-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/26ef51aa7932/fpsyg-12-762819-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/a059f7d3b29e/fpsyg-12-762819-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/5d469868e052/fpsyg-12-762819-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/dc64f2a00a24/fpsyg-12-762819-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/604cb9d13da6/fpsyg-12-762819-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3a35/8929398/26ef51aa7932/fpsyg-12-762819-g005.jpg

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本文引用的文献

[1]
Application of Deep Learning on Student Engagement in e-learning environments.

Comput Electr Eng. 2021-7

[2]
College students' use and acceptance of emergency online learning due to COVID-19.

Int J Educ Res Open. 2020

[3]
Projection of E-Learning in Higher Education: A Study of Its Scientific Production in Web of Science.

Eur J Investig Health Psychol Educ. 2021-1-10

[4]
Higher Education in Times of COVID-19: University Students' Basic Need Satisfaction, Self-Regulated Learning, and Well-Being.

AERA Open. 2021-3-15

[5]
The COVID-19 pandemic and E-learning: challenges and opportunities from the perspective of students and instructors.

J Comput High Educ. 2022

[6]
Gender Differences in Digital Learning During COVID-19: Competence Beliefs, Intrinsic Value, Learning Engagement, and Perceived Teacher Support.

Front Psychol. 2021-3-30

[7]
Learning during COVID-19: the role of self-regulated learning, motivation, and procrastination for perceived competence.

Z Erziehwiss. 2021

[8]
A Bibliometric Network Analysis of Coronavirus during the First Eight Months of COVID-19 in 2020.

Int J Environ Res Public Health. 2021-1-22

[9]
Mapping the Scientific Literature on COVID-19 and Mental Health.

Psychiatr Danub. 2020

[10]
QLearn: Towards a framework for smart learning environments.

Procedia Comput Sci. 2020

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