Investigating Students' Satisfaction with Online Collaborative Learning During the COVID-19 Period: An Expectation-Confirmation Model.

COVID-19 Expectation-confirmation model Online collaborative learning Satisfaction

Journal

Group decision and negotiation
ISSN: 1572-9907
Titre abrégé: Group Decis Negot
Pays: Netherlands
ID NLM: 101769514

Informations de publication

Date de publication:
2023
Historique:
accepted: 27 03 2023
medline: 12 6 2023
pubmed: 12 6 2023
entrez: 12 6 2023
Statut: ppublish

Résumé

The recent outbreak of COVID-19 posed discontinuous disruption to traditional learning modes worldwide. In order to keep social distance, online collaborative learning has become a necessity during the pandemic. However, our understanding of students' well-being and satisfaction with online collaborative learning is limited, especially during the COVID-19 period. Leveraging expectation confirmation theory, this study focuses on the triggers and inhibitors of students' cognitive load during online collaborative learning process and their subsequent satisfaction with the learning mode during the pandemic. We used a mixed-method approach in this study. We conducted a qualitative study with interview data and a quantitative study with surveys. The results indicate several psychological and cognitive antecedents of students' cognitive load during online collaborative learning. Findings also indicate that a high level of cognitive load will decrease students' perceived usefulness of the online learning platform and expectation confirmation, thus leading to a low level of satisfaction with online collaborative learning. This study can provide theoretical and practical implications for a better understanding of online student groups' satisfaction with online collaborative learning during the COVID-19 period.

Identifiants

pubmed: 37304175
doi: 10.1007/s10726-023-09829-x
pii: 9829
pmc: PMC10105153
doi:

Types de publication

Journal Article

Langues

eng

Pagination

749-778

Informations de copyright

© The Author(s), under exclusive licence to Springer Nature B.V. 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Déclaration de conflit d'intérêts

Conflict of interestNone.

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Auteurs

Xusen Cheng (X)

School of Information, Renmin University of China, Beijing, China.

Ying Bao (Y)

School of Economics and Management, China University of Petroleum, Beijing, China.

Bo Yang (B)

School of Information, Renmin University of China, Beijing, China.

Sihua Chen (S)

School of Information Management, Jiangxi University of Finance and Economics, Nanchang, China.

Yiting Zuo (Y)

School of Economics and Management, China University of Petroleum, Beijing, China.

Mikko Siponen (M)

Faculty of Information Technology, University of Jyväskylä, Jyväskylä, Finland.

Classifications MeSH