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

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Referred Conference Paperpeer-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 languageEnglish
Title of host publicationProceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350398144
DOIs
Publication statusPublished - 28 Dec 2022
Event9th IEEE International Conference on Behavioural and Social Computing, BESC 2022 - Matsuyama, Japan
Duration: 29 Oct 202231 Oct 2022

Publication series

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

Conference

Conference9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
Country/TerritoryJapan
CityMatsuyama
Period29/10/2231/10/22

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

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