Niveaux d’explicitation en mathématiques chez des étudiants universitaires
Bibliographic record
Abstract
Cet article propose un modèle par niveau des efforts d’explicitation qui peuvent être mis à contribution dans l’apprentissage des mathématiques. L’analyse des liens entre la formation fondamentale et les effets observés dans l’application des mathématiques a mis en évidence l’importance d’un tel travail d’explicitation. En effet, ceux qui font montre de compétences supérieures dans l’application des mathématiques investissent davantage, et de façon personnelle et autonome, dans l’explicitation des contenus d’apprentissage : lecture, questionnement, argumentation, utilisation des définitions, réécriture du cours, confection de résumés, tableaux de synthèse, etc. Il ressort de cette étude que le travail d’explicitation dans l’apprentissage des mathématiques doit non seulement favoriser la compréhension du sens, mais aussi viser la structuration des concepts mathématiques.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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; both teacher heads agree on what is shown here.
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".