1. La communauté d’apprentissage : une approche innovante au développement pédagogique des formateurs
Bibliographic record
Abstract
De 2010 à 2011, je me suis engagée, en tant que conseillère en pédagogie au Centre d’études et de formation en enseignement supérieur à l’Université de Montréal, dans un processus d’accompagnement et d’étude d’une communauté d’apprentissage. Cette communauté d’apprentissage regroupait des professionnels de la Direction de la santé publique qui donnaient de la formation à des étudiants provenant de différents programmes de l’Université. Étant donné les effets positifs de la participation à la communauté sur la pratique d’enseignement de ces formateurs, j’ai souhaité partager mon expérience avec les enseignants universitaires et les personnes qui les accompagnent dans leur développement pédagogique. Plus particulièrement, mon article a pour but de fournir des informations sur ce qu’est une communauté d’apprentissage, des moyens pratiques de mise en œuvre de ce type de dispositif ainsi que de ses retombées possibles.
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 | 0.045 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.007 | 0.012 |
| Scholarly communication | 0.014 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".