Investigating students' interaction patterns and dynamic learning sentiments in online discussions

Chang-Qin Huang, Zhong-Mei Han, Ming-Xi Li, Morris Siu-yung Jong, Chin-Chung Tsai

Research output: Contribution to journalArticlepeer-review

60 Citations (Scopus)


Convincing evidence found by educators and psychologists shows that students' interaction patterns of online discussions significantly affect their learning process, and are related to their learning sentiments. By using both quantitative content and lag sequential analysis, this study investigated students' interaction patterns and dynamic learning sentiments by performing seven types of learning tasks on an asynchronous discussion platform. The research participants were 38 postgraduate students. The results revealed that, compared to students performing the individual-oriented learning tasks, those performing the group-oriented ones had more diverse learning sentiments and interaction patterns, and deeper interactions with regard to learning sentiments. In addition, their learning sentiments exhibited a periodic feature during the process of online learning. Based on the results, we presented a four-phase model for describing a process of diverse interactions in online learning environments. In particular, this model characterizes the interactions with dynamic learning sentiments including generation, collision and integration, refinement, as well as stability.
Original languageEnglish
JournalComputers and Education
Publication statusPublished - 2019

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