Investigating user satisfaction of university online learning courses during the COVID-19 epidemic period

Date
2021-01-05
Authors
Zuo, Yiting
Cheng, Xusen
Bao, Ying
Zarifis, Alex
Journal Title
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Volume Title
Publisher
Volume
Number/Issue
Starting Page
1139
Ending Page
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Abstract
Online learning has been expanding for some time but the forced move to it due to the outbreak of COVID-19 has created new issues. This study set out to investigate the impact mechanism of online learning user satisfaction from the perspective of cognitive load in the era of COVID-19 and explore ways to optimize cognitive load in teaching practice. Semi-structured interviews were conducted for the empirical analysis. The coding process of the interviews yielded several antecedents of cognitive load in the online learning process. We also proposed a theoretical model based on the literature review and data analysis. Findings of the qualitative analysis indicate that the antecedents of cognitive load are multi-dimensional and the user's satisfaction with the online learning platform mainly consists of the expected confirmation of the information system and the perceived usefulness. These findings can help us think backward about optimizing user satisfaction with online learning in the context of COVID-19 breakout.
Description
Keywords
Decision Support for Smart City, satisfaction, covid-19, online learning, user
Citation
Extent
10 pages
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Geographic Location
Time Period
Related To
Proceedings of the 54th Hawaii International Conference on System Sciences
Rights
Attribution-NonCommercial-NoDerivatives 4.0 International
Rights Holder
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