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International Journal of Computer-Supported Collaborative Learning

Erschienen in:

04.03.2023

An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL

verfasst von: Lanqin Zheng, Miaolang Long, Jiayu Niu, Lu Zhong

Erschienen in: International Journal of Computer-Supported Collaborative Learning | Ausgabe 1/2023

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Abstract

Learning engagement has gained increasing attention in the field of education. Previous studies have adopted conventional methods to analyze learning engagement, but these methods cannot provide timely feedback for learners. This study analyzed automated group learning engagement via deep neural network models in a computer-supported collaborative learning (CSCL) context. A quasi-experimental research design was implemented to examine the effects of the automated group learning engagement analysis and feedback approach on collaborative knowledge building, group performance, socially shared regulation, and cognitive load. In total, 120 college students participated in this study; they were assigned to 20 experimental groups and 20 control groups of three students each. The students in the experimental groups adopted the automated group learning engagement analysis and feedback approach, whereas those in the control groups used the traditional online collaborative learning approach. Both quantitative and qualitative data were collected and analyzed in depth. The results indicated significant differences in group learning engagement, group performance, collaborative knowledge building, and socially shared regulation between the experimental and control groups. The proposed approach did not increase the cognitive load for the experimental groups. The implications of the findings can potentially contribute to improving group learning engagement and group performance in CSCL.

Vorheriger Artikel Fostering regulatory processes using computational scaffolding

Nächster Artikel Visions of the good in computer-supported collaborative learning: unpacking the ethical dimensions of design-based research

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Titel: An automated group learning engagement analysis and feedback approach to promoting collaborative knowledge building, group performance, and socially shared regulation in CSCL
verfasst von: Lanqin Zheng
Miaolang Long
Jiayu Niu
Lu Zhong
Publikationsdatum: 04.03.2023
Verlag: Springer US
Erschienen in: International Journal of Computer-Supported Collaborative Learning / Ausgabe 1/2023
Print ISSN: 1556-1607
Elektronische ISSN: 1556-1615
DOI: https://doi.org/10.1007/s11412-023-09386-0

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