Exploring the Factors of Undergraduate Learners’ Engagement and Knowledge Sharing for Sustainable hMOOC Learning
Bibliographic record
Abstract
In the post-pandemic era, the application of MOOC is essential to improve the quality and flexibility of higher education. This study aims to explore how factors from personal, environmental, and social level influence learners’ engagement and knowledge sharing in the context of hybrid MOOC (hMOOC) learning. Through random sampling, this study adopted a self-administered questionnaire to survey undergraduate students in China based on structural equation modeling (SEM). The results revealed motivation belief, system and relational quality had positive effects on learner engagement while content, instructor and relational quality also exerted positive effects on knowledge sharing. Meanwhile, learner engagement positively influenced knowledge sharing in hMOOC learning. However, system quality significantly affected knowledge sharing and instructor quality significantly affected learner engagement. Furthermore, content quality indirectly affected learner engagement via motivation belief. And learner engagement mediated the relationship between motivation belief and knowledge sharing behavior. These findings suggested that instructors, MOOC technician and administrator should take into consideration personal, environmental, and social factors to redesign an active engaging and sharing learning environment for achieving hybrid learning success.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".