Identifying patterns of epistemic emotions with respect to interactions in massive online open courses using deep learning and social network analysis

Zhong Mei Han, Chang Qin Huang*, Jian Hui Yu, Chin Chung Tsai

*此作品的通信作者

研究成果: 雜誌貢獻期刊論文同行評審

4 引文 斯高帕斯(Scopus)

摘要

Convincing evidence found by educators and psychologists shows that learners' interactions in discussion forums in massive online open courses (MOOC) overwhelmingly affect their epistemic emotions. In a MOOC context, epistemic emotions, such as the experiences of curiosity, enjoyment, confusion, and anxiety, are caused by the cognitive equilibrium or incongruity between new information and existing knowledge while learning via a MOOC course. Therefore, uncovering the relationships among epistemic emotions and interactions from large-scale MOOC data is an important task. By gathering multiple data generated by 1190 Chinese learners, this study employed a combination method of deep learning and social network analysis (SNA) to identify patterns of epistemic emotions with respect to interactions on a MOOC platform. The results revealed that four patterns, identified from core, neighbor, scattered, and peripheral learners, tended to expand relationships by votes and construct deep communication by comment and reply interactions. Of particular interest, the core and neighbor learners' patterns demonstrated significantly higher interactions and epistemic emotions than the scattered and peripheral learners’ patterns.

原文英語
文章編號106843
期刊Computers in Human Behavior
122
DOIs
出版狀態已發佈 - 2021 9月

ASJC Scopus subject areas

  • 藝術與人文(雜項)
  • 人機介面
  • 心理學(全部)

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