TY - GEN
T1 - Online learning research in the era of COVID-19
T2 - 9th IEEE International Conference on Behavioural and Social Computing, BESC 2022
AU - Chen, Xieling
AU - Zhang, Ruofei
AU - Zou, Di
AU - Cheng, Gary
AU - Xie, Haoran
AU - Wang, Fu Lee
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - COVID-19
KW - Online learning
KW - bibliometrics
KW - preliminary analysis
KW - research landscape
KW - topic modeling
UR - http://www.scopus.com/inward/record.url?scp=85146430554&partnerID=8YFLogxK
U2 - 10.1109/BESC57393.2022.9994896
DO - 10.1109/BESC57393.2022.9994896
M3 - Conference contribution
AN - SCOPUS:85146430554
T3 - Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
BT - Proceedings of the 2022 IEEE International Conference on Behavioural and Social Computing, BESC 2022
Y2 - 29 October 2022 through 31 October 2022
ER -