Un dispositif plurilingue d ’enseignement de l’orthographe grammaticale française pour favoriser les apprentissages d’élèves bi/plurilingues au secondaire
Bibliographic record
Abstract
Learning French grammar spelling (GS) is particularly difficult for first-and second-language students, including in Quebec. However, certain teaching practices, such as integrated approach and metacognitive dictations, have shown positive effects on students ’ GS. In this study, we designed a teaching method inspired by these practices, integrating plurilingual pedagogy as well to include the bi/plurilingual profiles of students in French Quebec schools. We then tested a “plurilingual method” with Grade 7 students ( n = 79) and compared its effects with those of a “monolingual method” ( n = 70) and traditional GS teaching practices ( n = 46). Using a dictation and a written production for pretest, immediate and delayed posttest, we found that both the plurilingual and monolingual methods contribute significantly more to the development of GS than traditional teaching practices, especially the plurilingual method over time.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".