Analyzing Learning Sentiments on a MOOC Discussion Forum Through Epistemic Network Analysis
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".