L’acquisition de la langue orale par l’entremise de tâches de centres d’apprentissage de littératie dans des classes d’immersion française
Bibliographic record
Abstract
This qualitative exploratory study examined the language/literacy tasks performed by elementary students from six elementary French Immersion (FI) classrooms. Various literacy tasks were performed as students rotated through different literacy centres/stations which had been pre-planned by their teachers. Specifically, researchers investigated students’ oral production and opportunities for extended oral output when working at independent learning centres/stations in order to identify key principles for creating literacy-enhancing tasks suitable for developing language literacy skills within second language (L2) contexts. Data were collected through classroom observations ( n = 23) to identify the types of literacy/language tasks proposed to L2 students, the nature of communicative functions, the targeted learning outcomes, and principles of effective L2 learning tasks. Results demonstrate the importance of adapting pedagogical practices, such as literacy centres/stations, borrowed from the first language teaching contexts to maximize L2 literacy/language learning and meet the specific needs of FI students. Results also highlighted the importance of ongoing professional learning opportunities for FI teachers specific to their L2 teaching contexts. Researchers propose principles for creating literacy/language tasks that promote oral language learning in FI contexts.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".