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

Yiting Zuo, Xusen Cheng, Ying Bao, Alex Zarifis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationProceedings of the 54th Annual Hawaii International Conference on System Sciences, HICSS 2021
EditorsTung X. Bui
PublisherIEEE Computer Society
Pages1139-1148
Number of pages10
ISBN (Electronic)9780998133140
Publication statusPublished - 2021
Externally publishedYes
Event54th Annual Hawaii International Conference on System Sciences, HICSS 2021 - Virtual, Online
Duration: 4 Jan 20218 Jan 2021

Publication series

NameProceedings of the Annual Hawaii International Conference on System Sciences
Volume2020-January
ISSN (Print)1530-1605

Conference

Conference54th Annual Hawaii International Conference on System Sciences, HICSS 2021
CityVirtual, Online
Period4/01/218/01/21

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