Using AI-empowered assessments and personalized recommendations to promote online collaborative learning performance
Lanqin Zheng, Yunchao Fan, Lei Gao, Zichen Huang, Bodong Chen, Miaolang Long
Beijing Normal University University of Pennsylvania
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摘要与影响
As an effective form of pedagogy, online collaborative learning has received increasing application in the field of education. However, learners often feel frustrated with regard to knowledge building, cognitive engagement, and socially shared regulation. To solve these problems, the current study proposed an approach featuring artificial intelligence (AI)-empowered assessments and personalized recommendations. The purpose of this quasi-experimental study was to examine the effect of the proposed approach on collaborative learning performance. In total, 135 college students, who were divided into three conditions, participated in the current study. The results of both quantitative and qualitative analysis indicated that the proposed approach could substantially enhance collaborative knowledge building, cognitive engagement, socially shared regulated behaviors, and group performance. The findings are discussed thoroughly alongside their pedagogical and technological implications.
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学科主题
社会科学Innovative Teaching and Learning Methods
Intelligent Tutoring Systems and Adaptive Learning · Artificial Intelligence in Education
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