La différenciation pédagogique du point de vue d’enseignants québécois : quelles différences pour les pratiques d’enseignement en contexte d’entrée dans l’écrit?
Bibliographic record
Abstract
L’adaptation de l’enseignement aux besoins de l’eleve est aujourd’hui reconnue essentielle a sa reussite scolaire. Cependant, peu de progres semble avoir ete realise durant la derniere decennie, en classe ordinaire, dans l’accompagnement des eleves presentant des besoins differents (McLeskey & Waldron, 2002). Notre recherche vise a rendre compte de pratiques declarees de differenciation pedagogique instaurees en contexte d’entree dans l’ecrit par 20 enseignants consideres exemplaires. Globalement, les resultats de notre etude montrent que ces pratiques se traduisent surtout par l’adaptation du soutien offert aux eleves et par l’adaptation des strategies et methodes d’enseignement aux caracteristiques des eleves. Toutefois, les enseignants interroges semblent manquer de reperes face aux pratiques d’evaluation formelle des acquis en contexte de differenciation. Mots-cles : Differenciation, pratiques exemplaires, literacie, enseignement elementaire
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Qualitative | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Qualitative | low |
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".