L’apprentissage de l’orthographe par la lecture-écriture : combien d’occurrences pour favoriser l’acquisition?
Bibliographic record
Abstract
Il se dégage un consensus dans la littérature scientifique sur l’importance du décodage dans l’acquisition des connaissances orthographiques. Ainsi, l’hypothèse de l’autoapprentissage par la lecture postule qu’un bon décodage permet de mémoriser la forme orthographique. D’autres études mesurent l’impact du traitement visuel et de la graphomotricité dans l’apprentissage de l’orthographe lexicale. Que ce soit pour l’un ou l’autre de ces facteurs le nombre de présentations ne fait pas l’unanimité. L’objectif de notre recherche est de mesurer l’effet de dix lectures-écritures sur un apprentissage de l’orthographe lexicale chez des enfants de 2e à la 5e année de primaire. Les résultats révèlent qu’à partir de la cinquième lecture-écriture, le bénéfice apporté par chacune des lectures-écritures supplémentaires reste stable.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".