Étude des liens entre les variables sociodémographiques et scolaires par rapport au rendement des élèves du primaire et à la perception des enseignants des difficultés d’apprentissage en mathématiques
Bibliographic record
Abstract
Dans cette recherche, nous avons vérifié quels sont les principaux facteurs explicatifs sous-jacents au rendement des élèves en mathématiques ainsi qu’à la perception des enseignants à l’égard des difficultés d’apprentissage. Pour ce faire, nous avons mis en oeuvre un devis corrélationnel impliquant la participation de dix-neuf enseignants et de 262 élèves du primaire. Les analyses de régression réalisées permettent de relever que le rendement en résolution de problèmes mathématiques est principalement lié aux habiletés en lecture ainsi qu’au degré scolaire des élèves. Par ailleurs, la perception de l’enseignant du rendement des élèves en mathématiques entretient essentiellement un rapport avec l’indice du seuil de faible revenu.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".