Analyse de qualité d’un MOOC : le point de vue des étudiants
Bibliographic record
Abstract
L’engouement qui faisait déclarer au New York Times que 2012 était l’« année du MOOC » semble désormais faire place au regard plus critique que porte la recherche sur le phénomène. En ce sens, le défi de la qualité est aussi important dans les MOOC que dans le domaine de la FAD. La présente recherche a pour objectif d’examiner l’appréciation que 631 apprenants font de leur expérience de deux MOOC du HEC Montréal, à partir de leurs perceptions et d’une approche inductive non préalablement fondée sur des a priori théoriques, à l’aide de trois questions ouvertes. Il émerge des résultats des critères d’évaluation se rapprochant de ceux utilisés dans les cadres de qualité en FAD, mais opérationnalisés d’une manière très spécifique. Ainsi, une attention toute particulière devrait être accordée au contenu (liens théorie-pratique, accessibilité des contenus), aux prestations enseignantes et à la clarté des tests.
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 | 0.027 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".