Les étudiants ont la parole : typologie des caractéristiques des activités technologiques de langue
Bibliographic record
Abstract
Cet article présente une recherche qualitative s’appuyant sur une analyse \nde construits (Kelly, 1995) réalisée auprès de 71 étudiants inscrits dans \ndes cours de français langue seconde dans cinq universités canadiennes. \nLes objectifs poursuivis par cette recherche sont d’une part, de réaliser \nun recensement d’activités technologiques de langue tels que les \nétudiants les ont utilisées dans leurs cours de français et d’autres \npart, d’établir une catégorisation des caractéristiques de ces activités. \nTout en s’inspirant de certains aspects de modèles existant (Chamberland, \nLavoie, et Marquis, 1996; Burnett (2000); Poellbhuber et Boulanger (2001); \nPerrault (2003); Cantin (2008), cet article propose une typologie de \nsix grands types d’activités basée sur sept continuums bipolaires. Cette \ntypologie de caractéristiques présente des profils différents pour chaque \ntype d’activité et offre des retombées pédagogiques avantageuses pour les \nprofesseurs de langue. \n \n \nThis paper presents the results of a qualitative construct analysis \n(Kelly, 1995) survey conducted with 71 students enrolled in French as a \nsecond language courses at five Canadian universities. The objectives of \nthis research are to conduct a census of language technological \nactivities that students have used in their French classes and to \ncategorize their characteristics. A typology of six major types of \nactivities based on seven bipolar continua is proposed. This typology \npresents different profiles for each type of activity and provides \neducational tools for language teachers. The typology draws on some \naspects of existing models from Chamberland, Lavoie, and Marquis (1996), \nBurnett (2000), Poellbhuber and Boulanger (2001), Perrault (2003) and \nCantin (2008).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.000 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".