LES CONNAISSANCES MATHEMATIQUES ET DIDACTIQUES CHEZ LES FUTURS MAITRES DU PRIMAIRE: QUATRE CAS A L'ETUDE
Bibliographic record
Abstract
Les futurs enseignants et enseignantes présentent de nombreuses lacunes dans l’apprentissage de la didactique des mathématiques, lesquelles sont souvent accentuées par des attitudes négatives véhiculées face aux mathématiques. Ces lacunes et ces attitudes ne sont pas sans conséquence quant à l’enseignement de cette matière aux enfants. Ces préoccupations étant à l’origine de notre étude, cet article traite des difficultés qu’éprouvent les futurs maîtres en fin de formation à effectuer l’intégration de leurs connaissances mathématiques et didactiques en classe d’enseignement. Mots‐clés: Didactique des mathématiques, futurs enseignants, difficultés en mathématiques, attitudes, réflexion critique. Preservice teachers demonstrate many knowledge gaps in learning to teach mathematics and these gaps are often accentuated by their negative attitudes to the subject. These gaps and attitudes can be important when teaching this material to children. Our study, which grew out of these concerns, discusses the difficulties still being experienced by student teachers at the end of their teacher education program in trying to integrate their knowledge of both mathematics and mathematics teaching methods. Key words: Didactics, preservice teachers, mathematical difficulties, attitudes, critical reflexion
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".