Compétences orales et outils de communication web dans un projet de télécollaboration pour l’apprentissage du français langue étrangère
Bibliographic record
Abstract
L’application des nouvelles technologies a l’enseignement des langues ouvre de nouvelles voies a l’experimentation et a la recherche de methodes et d’outils qui puissent favoriser l’acquisition des competences langagieres et interculturelles chez les apprenants. Malgre l’expansion sur Internet de toutes sortes d’outils de communication audiovisuels, les strategies de production orale sont encore peu developpees en classe de francais langue etrangere (FLE). Dans le cadre des projets actuels de telecollaboration la langue ecrite prend une place preponderante face a la langue orale. Le Projet Leon-Grenoble essaie de corriger cette situation en privilegiant les strategies et les pratiques pedagogiques pour la comprehension et la production orales. L’utilisation des tâches et outils Web pour la production orale des apprenants de FLE est ici fondamentale. Dans cet article nous analyserons tous ces elements, sans oublier l’un des plus determinants: la correction de la prononciation en relation avec les trois acteurs de cette recherche-action : apprenants, tuteurs et enseignants. The application of new technologies in language teaching opens up a range of options in the search for methods and tools favourable to both effective foreign language acquisition and knowledge of its culture. In spite of the growth of web-enabled audiovisual tools that enhance communication, oral production strategies are still poorly developed in the language classroom. Within the framework of pedagogical telecollaboration projects, written language is usually predominant. The Leon-Grenoble project tries to correct these discrepancies by trying to establish a balance between oral and written skills. Task-based oral production is here fundamental for learners of French as a Foreign Language (FLE). And even more crucial are the practices to correct pronunciation for the three agents involved in the educational context: teachers, tutors and students of FLE.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".