Apprenants à la recherche de collocatifs dans les dictionnaires électroniques: Analyse de l'interaction 'apprenant-tâche-dictionnaire à l'ordinateur
Bibliographic record
Abstract
Dans cet article, nous discutons de l’interaction apprenant-tâche-dictionnaire a l’ordinateur. Nous faisons un survol de la litterature en lexicographie pedagogique dediee a la relation apprenantdictionnaire. Nous presentons ensuite une etude experimentale ayant vise l’analyse des comportements observes a l’ordinateur d’apprenants de francais langue seconde (FLS) a la recherche de collocatifs dans deux dictionnaires electroniques grand public. Les resultats, bases sur une mesure d’efficience et d’efficacite avec laquelle les apprenants ont accompli deux microtâches d’encodage du texte, montrent un rapport assez etroit entre le succes de la tâche et les strategies de recherche (dans les dictionnaires) employees. Le contexte de l’etude est celui du developpement d’un prototype de dictionnaire electronique actif, centre sur des besoins specifiques d’encodage du texte des apprenants avances du FLS, besoins que comblent en partie les dictionnaires electroniques selectionnes pour cette etude et que viendrait davantage combler celui que nous proposons.This article examines the learner-task-dictionary interaction on the computer. It first presents an overview on the literature in pedagogical lexicography dedicated to the learner-dictionary relationship. It then reports on an experimental study which aimed to produce an analysis of FSL learners’ behaviours on the computer while searching for collocations in two wide-audience electronic dictionaries. The results, based on a measure of effectiveness and efficiency with which learners have accomplished two text encoding micro-tasks, show a narrow enough relationship between task success and dictionary look-up strategies. The context of the study is the one of the development of an electronic dictionary prototype centered on some specific FSL learners’ text encoding needs which are partly addressed by the selected dictionaries in this study and that the one proposed would further fulfill.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".