De la lecture à la résolution de problèmes: des habiletés spécifiques à développer
Bibliographic record
Abstract
La reussite des eleves en mathematiques, particulierement la reussite en resolution de problemes ecrits, a souvent ete associee aux competences en lecture. Si plusieurs recherches ont permis d’etablir clairement un lien entre le rendement en lecture des eleves et leur rendement en mathematiques, nous ne savons pas encore precisement quelles habiletes specifiques en lecture constituent les meilleurs indicateurs du rendement en resolution de problemes ecrits de mathematiques. Notre objectif consiste a preciser les habiletes liees a la lecture que les bons solutionneurs mettent a profit en contexte de resolution de problemes ecrits de mathematiques. Pour atteindre les objectifs de la recherche, nous avons utilise un devis de recherche quantitatif. L’echantillon de l’etude est compose de 73 eleves de 4e annee du primaire. Les participants ont complete deux epreuves de comprehension en lecture, soit un texte narratif et un texte informatif, ainsi qu’un ensemble de problemes ecrits de mathematiques. Les analyses de correlation nous ont permis d’etablir des liens entre les differentes variables a l’etude. Les resultats soutiennent que certaines habiletes specifiques en lecture constituent des indicateurs a privilegier afin de juger du rendement en resolution de problemes ecrits de mathematiques des eleves. Mots-cles : Mathematiques, resolution de problemes ecrits, situation-probleme, comprehension en lecture, enseignement primaire.
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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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".