Le manuel numérique en mathématique : le cas de la moyenne
Bibliographic record
Abstract
La présente étude examine les manuels numériques en mathématiques, et plus particulièrement, les stratégies et ressources utilisées pour la présentation de la moyenne. Même si l’enseignement de ce concept se résume souvent à son algorithme de calcul, il existe d’autres approches permettant de développer la compréhension de la moyenne. Ainsi, pour mieux comprendre la valeur ajoutée des manuels numériques en mathématiques, nous proposons d’examiner la présence du concept de moyenne dans 11 manuels numériques, de la 1re à la 3e secondaire. Nos résultats permettent de voir que même si certains manuels proposent des ajouts intéressants, il appert que d’autres procédures (total-répartition, nivelage et point d’équilibre) sont peu abordées, suggérant que l’enseignant devra utiliser d’autres ressources didactiques pour y arriver.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".