Les stratégies, l’engagement et l’ergonomie cognitive comme leviers pour l’enseignement / apprentissage des langues
Bibliographic record
Abstract
Ce premier numéro du 35e volume de Recherche et pratiques pédagogiques en langues de spécialité (RPPLSP) a été coordonné par Emilie Magnat et Rebecca Dahm pour l’association pour la recherche en didactique de l’anglais et en acquisition (Ardaa). Elles ont choisi pour thème les stratégies, l’engagement et l’ergonomie cognitive comme leviers pour l’enseignement / apprentissage des langues. Dans la continuité de la politique éditoriale de la revue, la plupart des textes rassemblés ici interrogent ces thématiques dans le cadre spécifique de l’enseignement supérieur et de la formation d’adultes, pour différentes langues (ici, l’anglais, le français et l’espagnol) et dans des contextes aussi variés que le contexte français, canadien ou algérien. Le questionnement sur les stratégies en particulier s’insère dans la lignée du précédent numéro de la revue « Réussite et échec en langues de spécialité » (https://apliut.revues.org/4348). Volume 35 of Teaching and Researching Languages of Specific Purposes (RPPLSP) opens with a number coordinated by Emilie Magnat and Rebecca Dahm from Ardaa (a French association of researchers in the domain of English learning and acquisition). Emilie Magnat and Rebecca Dahm have elected the questions of "Strategies, Engagement and Cognitive Ergonomics in Teaching and Learning Languages" as a theme for this number. In coherence with the editorial policy of the journal, most of the articles question these issues within the specific realm of Higher Education and adult training. They do so for different languages (English, French and Spanish) and in various contexts such as the French, Canadian or Algerian contexts. This number aptly falls in line with the preceding one on "Success and Failure in LSP" (https://apliut.revues.org/4348).
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.014 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 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".