Apprentissage coop�ratif et prises de parole en langue cible dans deux classes d'immersion
Bibliographic record
Abstract
Résumé Un des défis à relever en immersion linguistique, comme dans les cours de langue classiques, est de fournir aux élèves suffisamment d'occasions de parler et de communiquer en langue cible. La recherche montre que l'apprentissage coopératif, dont l'efficacité a été bien établie dans des contextes variés, constitue un outil susceptible d'aider à répondre à ce défi. Des activités d'apprentissage coopératif proposées par une des chercheuses ont été mises en place par les titulaires de deux classes de deuxième année primaire (élèves de 7 et 8 ans) en immersion partielle, l'une en allemand et l'autre en néerlandais. L'analyse des prises de parole au sein des deux groupes hétérogènes de quatre enfants par classe dont les interactions ont été enregistrées, met en évidence que chacun des enfants a pris la parole non seulement en français, mais aussi en langue cible, et que la répartition des interventions au sein des groupes est assez équitable. Abstract Providing students with enough opportunities to speak and communicate in the target language is one of the challenges faced by both linguistic immersion and conventional language courses. The study documented here shows that cooperative learning, the efficiency of which has been demonstrated in various contexts, could help educators meet this challenge. Cooperative learning activities suggested by one of the researchers were organized by the teachers of two Grade 2 classes (7- and 8-year-old students) in partial immersion (German in one case, Dutch in the other). The speech acts among two heterogeneous groups of four children per class were recorded and analysed. The article shows that every pupil spoke not only in their L1 but also in the target language and that speech acts were fairly equally distributed within each group. [End Page 225]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".