Une communauté de pratique en ligne pour accompagner le développement de pratiques enseignantes en soutien aux habiletés inférentielles chez les enfants de milieux défavorisés | An Online Community of Practice to Support the Development of Teaching Practices in Reading Comprehension
Bibliographic record
Abstract
L’apprentissage de compréhension de lecture (CL) est déterminant pour la persévérance et la réussite scolaires. Les enseignants se sentent peu outillés pour soutenir cet apprentissage et plus précisément le développement des habiletés inférentielles (St-Pierre et al., 2015). Cette recherche-action visait à accompagner des enseignantes du primaire dans leurs pratiques en CL afin de soutenir le développement des habiletés inférentielles chez leurs élèves en plus de viser à documenter la mise en place d’une communauté de pratique en ligne. À partir d’une analyse de contenu, nous avons ciblé l’évolution des cycles de développements professionnels des participantes et dégagé les thèmes sur lesquels portaient leurs apprentissages. Les résultats démontrent que les principaux apprentissages portent essentiellement sur les pratiques mises en place dans leur classe.Reading comprehension is critical to students' persistence and academic achievement. Teachers feel ill prepared to support this learning and more specifically the development of inferential skills (St-Pierre et al., 2015). This article presents the results of an action research study aiming at accompanying primary school teachers in their practices in order to support the development of the inferential skills of their students and at documenting the implementation of an online community of practice. Based on a content analysis, the study focused on the evolution of the participants' professional development cycles and identified the themes of their learning within the community of practice. The results show that their professional development mainly focused on classroom practices.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".