Développement d'une culture de données et de collaboration chez des enseignants travaillant en communauté d'apprentissage professionnelle
Bibliographic record
Abstract
Cet article presente les resultats d’une recherche-action-formation ou l’utilisation d’un logiciel d’analyse de donnees a servi de support technologique pour faciliter la collaboration des enseignants en communaute d’apprentissage professionnelle. La recherche fut menee dans une ecole francophone en milieu defavorise de la region de Quebec (Canada) aupres d’une equipe-ecole formee d’enseignants, d’orthopedagogues, d’une direction d’ecole et de chercheurs. Les resultats montrent qu’a certaines conditions, il est possible de faire evoluer une equipe-ecole comme communaute d’apprentissage professionnelle ou la collaboration entre les intervenants et la collecte de donnees probantes sont des elements essentiels. Toutefois, le peu de temps offert pour œuvrer en communaute d’apprentissage professionnelle et la surcharge de travail qu’occasionnent la collecte et l’analyse des donnees constituent des obstacles majeurs a surmonter afin d’assurer la reussite de toutes et tous.
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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 | 0.042 | 0.088 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 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".