Profiling students’ learning engagement in MOOC discussions to identify learning achievement: An automated configurational approach

In the Massive Online Open Course (MOOC) forum, learning engagement encompasses three fundamental dimensions—cognitive, emotional, and behavioral engagement—that intricately interact to jointly influence students' learning achievements. However, the interplay between multiple engagement dimensions and their correlations with learning achievement remain understudied, particularly across different academic disciplines. This study adopts an automated configurational approach that integrates bidirectional encoder representation from transformers (BERT) and fuzzy set qualitative comparative analysis (fsQCA) to explore the configurations of learning engagement, their connections with learning achievement, and variations across disciplines. Our analysis reveals a nuanced profile of learners' learning engagement, indicating the high-achieving individuals demonstrated more frequent posting and commenting behaviors and the high-level cognitive engagement than low-achieving individuals. Second, our analysis revealed multiple configurations where the coexistence or absence of factors at different levels of the cognitive, behavioral, and emotional dimensions significantly impacted learning achievement. Learners who conducted posting and replying behaviors, expressed positive emotions, and engaged in deep cognitive engagement tended to achieve superior learning outcomes. Third, there were significant differences in behavioral and emotional engagement among learners across different academic disciplines. Specifically, pure discipline learners were more inclined to engage in posting behaviors than the applied discipline learners. Across academic disciplines, positive emotions correlated strongly with higher achievement. These findings deepen our understanding of the multifaceted characteristics of learning engagement in MOOCs and highlight the importance of disciplinary distinctions, providing a foundation for educators and designers to optimize learners' MOOC effects and tailor learning experiences in diverse disciplinary contexts.