S'instruire des paroles d'élèves au sujet de l'étude de l'orthographe lexicale
Bibliographic record
Abstract
Resume La presente recherche s’est interessee a la situation d’etude de l’orthographe lexicale a la maison au moyen d’entretiens aupres de 272 eleves du premier cycle du primaire. Les resultats ont revele differentes manieres d’operer chez les eleves pour s’acquitter de cette tâche. Si une bonne part des eleves consacrait du temps pour etudier l’orthographe a la maison, 4 eleves sur 10 se faisaient dicter les mots par un parent sans consacrer prealablement du temps d’etude. Diverses strategies sont evoquees par les eleves pour encoder l’orthographe, mais ce sont les filles et les eleves sans difficulte qui ont declare utiliser le plus de strategies et celles-ci etaient davantage diversifiees que chez les garcons et les eleves en difficulte. Les eleves ont rapporte que les strategies etaient le plus souvent utilisees en copiant les mots avec modele. C’etaient majoritairement les meres qui soutenaient les enfants au regard de cette tâche scolaire a domicile. Abstract This study focuses on the learning process of vocabulary spelling at home. Data were collected using interviews with 272 students of Grades 1 and 2. Results reveal that students accomplish this task in different ways. A large proportion of students spend time studying spelling at home. However, 4 out of 10 students do not study vocabulary words prior to their parents reading them out loud. Students use various strategies to learn spelling, but girls and students without difficulties reported using more strategies compared to boys and students with difficulties. Students indicated that strategies are most often used when they copy words using a model. It is mostly mothers who support children during spelling study at home.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".