Enhancing Learner Participation in Online Discussion Forums in Massive Open Online Courses: The Role of Mandatory Participation
Bibliographic record
Abstract
Online discussion forums are an essential and standard setup in online courses to facilitate interactions among learners. However, learners' inadequate participation in online discussion forums is a long-standing challenge, which necessitates instructor intervention and the design consideration of online learning platforms. This research proposes and studies the role of mandatory participation, i.e., learners' participation in online course forums by instructors' requirements, in fostering their voluntary participation and boosting their learning performance. This novel effect link between mandatory participation and voluntary participation has not been assessed in previous research. An empirical study is conducted using a large-scale dataset of 27,767 learners from a leading massive open online course (MOOC) platform in China. The findings indicate that besides its direct effect on learning performance, learners' mandatory participation has a significant positive effect on their voluntary participation in online course forums, enhancing learning performance. Moreover, the effect of mandatory participation on voluntary participation varies across learner groups, being more prominent for early registrants than late registrants and part-time learners than full-time learners. This research contributes to the online learning literature by introducing mandatory participation as a viable approach to foster voluntary participation and boost learning performance through enhanced voluntary participation. It provides evidence on the effectiveness of the novel design feature of MOOC platforms that enables and facilitates the mandatory participation mechanism in online learning.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".