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 de construits (Kelly, 1995) réalisée auprès de 71 étudiants inscrits dans des cours de français langue seconde dans cinq universités canadiennes. Les objectifs poursuivis par cette recherche sont d’une part, de réaliser un recensement d’activités technologiques de langue tels que les étudiants les ont utilisées dans leurs cours de français et d’autres part, d’établir une catégorisation des caractéristiques de ces activités. Tout en s’inspirant de certains aspects de modèles existant (Chamberland, Lavoie, et Marquis, 1996; Burnett (2000); Poellbhuber et Boulanger (2001); Perrault (2003); Cantin (2008), cet article propose une typologie de six grands types d’activités basée sur sept continuums bipolaires. Cette typologie de caractéristiques présente des profils différents pour chaque type d’activité et offre des retombées pédagogiques avantageuses pour les professeurs de langue. This paper presents the results of a qualitative construct analysis (Kelly, 1995) survey conducted with 71 students enrolled in French as a second language courses at five Canadian universities. The objectives of this research are to conduct a census of language technological activities that students have used in their French classes and to categorize their characteristics. A typology of six major types of activities based on seven bipolar continua is proposed. This typology presents different profiles for each type of activity and provides educational tools for language teachers. The typology draws on some aspects of existing models from Chamberland, Lavoie, and Marquis (1996), Burnett (2000), Poellbhuber and Boulanger (2001), Perrault (2003) and Cantin (2008).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".