Analyzing Learning Sentiments on a MOOC Discussion Forum Through Epistemic Network Analysis - Yu, Jianhui (International Review of Research in Open and Distributed Learning)

(untitled) (Athabasca University Press (AU Press), 2025)
Sentiments expressed on massive open online course (MOOC) discussion forums significantly influence learning effectiveness and academic performance. The evolution of learning sentiments on MOOC discussion forums is a dynamic process; however, a gap exists in the current understanding of the interplay between evolving sentiments and their impact on MOOC efficacy. Consequently, to enhance MOOC effectiveness further empirical research is needed to uncover the underlying patterns and temporal dynamics of learning sentiments. This study collected online discussions from 158 MOOC participants and examined the discussions using epistemic network analysis to identify how learning sentiment patterns differed according to performance level and learning topics. The results showed that learning sentiment patterns were affected by both performance level and learning topics, with participants in the high-score group exhibiting stronger associations between engagement-neutral and neutral-frustration, and fewer connections between frustration-delight and frustration-boredom when compared to those in the low-score group. In addition, this study found that engagement was strongly linked to all learning topics in the high-score group, whereas for the low-score group, only engagement and experience showed strong connections. Based on these findings, we discuss the implications for learners and instructors in paving the way for the development of targeted interventions and instructional strategies tailored to optimize MOOC effectiveness.