Différencier les pratiques pédagogiques pour tenir compte de l’hétérogénéité : une question de compétence en gestion de classe ?
Bibliographic record
Abstract
Le contexte scolaire actuel au Québec, et particulièrement à Montréal, amène les enseignants à devoir développer des stratégies de pédagogie différenciée pour répondre aux besoins de tous. En effet, l’intégration des élèves handicapés ou en difficulté d’adaptation ou d’apprentissage ainsi que les différences socioéconomiques et culturelles sont des manifestations de l’hétérogénéité dont il faut tenir compte en enseignement. Des études menées au Québec semblent toutefois indiquer que de telles pratiques différenciées demeurent peu répandues. Il semble que, d’une part, la gestion de classe soit un déterminant de l’implantation des pratiques différenciées et, d’autre part, que cette bonne gestion de classe soit tributaire d’un haut niveau de sentiment d’efficacité personnelle à gérer la classe. Cet article présente un projet de recherche à devis mixte conçu pour explorer les relations entre les pratiques différenciées et le sentiment d’efficacité à gérer la classe chez des enseignants au primaire de Montréal. The current school context in Quebec, especially in Montreal, leading teachers to develop differentiated pedagogies to meet the needs of all. Indeed, the integration of students with special needs, socio-economic and cultural differences are examples of manifestations of heterogeneity to be observed in teaching. Yet, few teachers adopted such practices. Moreover, we know that, firstly, classroom management is a key to the implementation of differentiated practices and secondly, good classroom management depends on a high level of self-efficacy of classroom management. This article present a mixed method design study we will conduct to explore relationships between frequency of differentiated practices and self-efficacy of classroom management with primary teachers in Montreal.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".