Intégration des TICE sur le thème du lait en français appliqué à l’agronomie
Bibliographic record
Abstract
Le Cadre européen commun de référence pour l’apprentissage / enseignement des langues focalise la conception des programmes sur la spécificité du public, ses objectifs et ses besoins. Ainsi dans la partie « avertissement » (2001 : 5) dudit Cadre, on relève : « il est absolument essentiel de définir avec un maximum de précision des objectifs immédiatement valables au regard des besoins des apprenants et réalistes du point de vue de leurs caractéristiques et des moyens disponibles ». C’est dans ce but, que notre approche du français, au départ langue de spécialité, se situe maintenant plus précisément dans le français sur objectifs spécifiques. Professeurs dans une Université Polytechnique et dans une École Supérieure Technique, nous nous voyons contraints à devenir concepteurs du matériel didactique que nous utilisons. En effet, il n’existe pas encore sur le marché d’ouvrages ou de méthodes destinés à des publics limités de par leurs besoins particuliers. Après une analyse ciblée des besoins de nos apprenants, nous proposons dans cet article un exemple d’intégration des TICE dans une unité didactique sur un thème technique : le lait. Ce choix à la fois culturel et technique va nous permettre de traiter des objectifs professionnels, communicatifs et linguistiques. Mots clés : Cadre européen commun de référence pour les Langues (CECRL) – Français sur objectifs spécifiques (FOS) – Technologies de l’information et de la communication pour l’éducation (TICE) – Objectifs professionnels, communicatifs et linguistiques – Le lait. Abstract The Common European Framework of Reference for Languages centres the elaboration of the curricula on the basis of the specificity of the audience, its objectives and needs. In this vein, the section entitled “Recommendations”, reads as follows: “it is absolutely necessary to define with the highest accuracy and without any delay those valued aims which are best adapted to the learners’ needs and which conform to our teaching reality considering their defining characteristics and the resources available. With this aim in mind, we approach the teaching of French as a language for specific purposes. As teachers working at the Escuela Superior de Agrónomos we are forced to elaborate our own teaching materials due to a lack of them in the teaching market as well as to the specific needs that our students have. After carrying out a needs analysis, in this article we put forward an example of integration of ICT in a didactic unit elaborated around a technical topic, that of milk. This cultural and technical choice will allow us to deal with professional, communicative and linguistic aims. Key words: Common European Framework of Reference for Languages - French for specific purposes - ICT - Professional, communicative and linguistic aims - Milk -
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".