Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

Xieling CHEN; Ruofei ZHANG; Di ZOU; Gary CHENG; Haoran XIE; Fu Lee WANG

doi:10.1109/BESC57393.2022.9994896

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

Xieling CHEN^*, Ruofei ZHANG, Di ZOU, Gary CHENG, Haoran XIE, Fu Lee WANG

^*Corresponding author for this work

Department of Computing and Decision Sciences

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

Abstract

The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning.

Original language	English
Title of host publication	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350398144
DOIs	https://doi.org/10.1109/BESC57393.2022.9994896
Publication status	Published - 28 Dec 2022
Event	9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 - Matsuyama, Japan Duration: 29 Oct 2022 → 31 Oct 2022

Publication series

Name	Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022

Conference

Conference	9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
Country/Territory	Japan
City	Matsuyama
Period	29/10/22 → 31/10/22

Bibliographical note

Funding Information:
Figure 5 illustrates the annual number of funds for the 6571 online learning academic papers supported by eight agencies, including National Science Foundation, National Natural Science Foundation of China, Ministry of Science and Technology Taiwan, European Commission, United States Department of Health Human Services, National Institutes of Health, US Department of Education, and Projekt Deal. The result shows that research on online learning has been supported by governmental research sectors worldwide, especially in the USA, Mainland China, and Taiwan.

Publisher Copyright:
© 2022 IEEE.

Keywords

bibliometrics
COVID-19
Online learning
preliminary analysis
research landscape
topic modeling

Access to Document

10.1109/BESC57393.2022.9994896

Cite this

CHEN, X., ZHANG, R., ZOU, D., CHENG, G., XIE, H., & WANG, F. L. (2022). Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling. In Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022 (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BESC57393.2022.9994896

CHEN, Xieling ; ZHANG, Ruofei ; ZOU, Di et al. / Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling. Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022).

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title = "Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling",

abstract = "The global rampancy of COVID-19 has caused profound changes in education sectors. Perhaps the most salient change is the shift of the instructional paradigm from face-to-face instruction to fully online learning. To address the challenges facing the education sector, researchers and educational practitioners have extensively investigated the transition in teaching mode under COVID-19, with a growing contribution to a range of topics in relation to online learning. Against this backdrop, it is necessary to gain a comprehensive understanding of the major hotspots and issues of online learning so as to develop appropriate and effective policies on strategic (re-)allocation of resources to more critical initiatives. This study aims to adopt bibliometrics and topic modeling to identify prominent research topics on online learning under COVID-19 from the large-scale, unstructured text of research publications. Specifically, structural topic modeling will be used to identify predominant topics concerned by scholars working in the field of online learning research. The non-parametrical Mann-Kendell trend test will also be applied to uncover the developmental tendency of each identified topic. In addition, the correlations among the key topics will be revealed and visualized by hierarchical clustering analysis. Based on the analytical results, suggestions will be made to facilitate educational policy formulation to promote the development and effective implementation of technological, scientific, and pedagogical activities of online learning.",

keywords = "bibliometrics, COVID-19, Online learning, preliminary analysis, research landscape, topic modeling",

author = "Xieling CHEN and Ruofei ZHANG and Di ZOU and Gary CHENG and Haoran XIE and WANG, {Fu Lee}",

note = "Funding Information: Figure 5 illustrates the annual number of funds for the 6571 online learning academic papers supported by eight agencies, including National Science Foundation, National Natural Science Foundation of China, Ministry of Science and Technology Taiwan, European Commission, United States Department of Health Human Services, National Institutes of Health, US Department of Education, and Projekt Deal. The result shows that research on online learning has been supported by governmental research sectors worldwide, especially in the USA, Mainland China, and Taiwan. Publisher Copyright: {\textcopyright} 2022 IEEE.; 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 ; Conference date: 29-10-2022 Through 31-10-2022",

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CHEN, X, ZHANG, R, ZOU, D, CHENG, G, XIE, H & WANG, FL 2022, Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling. in Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022, Institute of Electrical and Electronics Engineers Inc., 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022, Matsuyama, Japan, 29/10/22. https://doi.org/10.1109/BESC57393.2022.9994896

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling. / CHEN, Xieling; ZHANG, Ruofei; ZOU, Di et al.
Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc., 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022).

Research output: Book Chapters | Papers in Conference Proceedings › Conference paper (refereed) › Referred Conference Paper › peer-review

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AU - ZOU, Di

AU - CHENG, Gary

AU - XIE, Haoran

AU - WANG, Fu Lee

N1 - Funding Information: Figure 5 illustrates the annual number of funds for the 6571 online learning academic papers supported by eight agencies, including National Science Foundation, National Natural Science Foundation of China, Ministry of Science and Technology Taiwan, European Commission, United States Department of Health Human Services, National Institutes of Health, US Department of Education, and Projekt Deal. The result shows that research on online learning has been supported by governmental research sectors worldwide, especially in the USA, Mainland China, and Taiwan. Publisher Copyright: © 2022 IEEE.

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CHEN X, ZHANG R, ZOU D, CHENG G, XIE H, WANG FL. Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling. In Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022. Institute of Electrical and Electronics Engineers Inc. 2022. (Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022). doi: 10.1109/BESC57393.2022.9994896

Online learning research in the era of COVID-19: Bibliometric analysis and topic modeling

Abstract

Publication series

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Bibliographical note

Keywords

Access to Document

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