Développement d'un dispositif didactique exploitant des stratégies de lecture en classe d'histoire de 4e secondaire
Bibliographic record
Abstract
Les recherches ont d’ores et deja demontre que l’acte de lire est omnipresent dans toutes les disciplines du cursus scolaire, dont l’histoire (Blaser, 2008; Blouin, 2020; Deslauriers, 2007). Neanmoins, les enseignants d’histoire ne sont pas suffisamment outilles pour aider leurs eleves en ce qui concerne leur difficulte a lire en histoire (Chartrand, 2009) et peu de ressources materielles s’adressant aux enseignants d’histoire du Quebec sont utilisables directement en salle de classe. Dans l’optique d’offrir un soutien aux enseignants et aux eleves en prevision de l’epreuve unique d’histoire de 4e secondaire (Ministere de l’Education, de l’Enseignement Superieur et de la Recherche [MEESR], 2015), qui se base en grande partie sur la lecture de documents historiques (Blouin, 2020), la question se pose : Quelles sont les composantes essentielles d’un dispositif didactique exploitant les strategies de lecture en classe d’histoire de 4e secondaire? Afin de repondre a cette question, une recherche developpement (Harvey et Loiselle, 2009; Nonnon, 1993, Van der Maren, 2003) en six phases a permis de developper un dispositif didactique, de mettre en lumiere le processus de developpement de ce dispositif didactique ainsi que ses composantes essentielles. Cette recherche amene egalement un questionnement sur le role reel des dispositifs didactiques comme soutien en lecture aux enseignants et aux eleves.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".