Exploring the relationship between learning sentiments and cognitive processing in online collaborative learning: A network analytic approach

计算机科学 协作学习 认知 在线学习 教育技术 心理学 数学教育 人工智能 多媒体 知识管理 神经科学
作者
Jun-min Ye,Jin Zhou
出处
期刊:Internet and Higher Education [Elsevier]
卷期号:55: 100875-100875 被引量:14
标识
DOI:10.1016/j.iheduc.2022.100875
摘要

Evidence suggests that learning sentiments are inextricably related to cognitive processing, and the exploration of the relationship remains to be an important research topic. This study collected discourse data from 40 college students in online collaborative learning activities. Epistemic network analysis (ENA) was employed to explore the connection between learning sentiments and cognitive processing and compare the ENA network characteristics of the higher- and lower-engagement groups. The results indicated that there was a joint connection between understand-analyze-neutral, and insightful sentiments had more association with neutral sentiments and understanding. Besides, distinctions existed between higher- and lower-engagement groups with respect to the association between learning sentiments and cognitive processing. The higher-engagement group had stronger associations around positive and confused sentiments, while the lower-engagement group had stronger associations around off-topic discussion. The findings of this research may serve as a reference for designing and implementing collaborative learning activities to increase cognitive levels. • We examined the relationship between learning sentiment and cognitive processing. • Epistemic network analysis was used to extract more details about this relationship. • Distinctions existed between higher- and lower-engagement groups in sentiment and cognition. • The higher-engagement group had stronger associations around positive and confused sentiments. • Higher behavioral engagement does not necessarily represent higher cognitive processing.
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