Optimiser la qualité d'une REL en amont par un bon design pédagogique
Bibliographic record
Abstract
Support de présentation (PowerPoint) d'une communication donnée au Congrès de l'ACFAS 2024 lors du Colloque 635 "Les ressources éducatives libres (REL) en contextes francophones canadiens : recherche et intégration aux pratiques éducatives au postsecondaire". Dans cette communication, nous identifions tout d’abord les principaux critères de qualité des REL identifiés dans la littérature. Nous examinerons ensuite comment le design pédagogique peut jouer un rôle fondamental pour prévenir les lacunes et les erreurs, et plus généralement pour optimiser la qualité d’une REL, à partir des tendances actuelles du domaine et de notre propre expérience. Dans une perspective plus large, le cycle de vie complet d’une REL et de ses dérivés sera considéré.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.008 |
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; both teacher heads agree on what is shown here.
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".