Cliquer, glisser, dactylographier ou sélectionner dans un menu déroulant : manipulations préférées des étudiants universitaires<br> Click, slide, type or select in a pop-up menu: Favourite manipulations of French-as-a-second-language university students
Bibliographic record
Abstract
Résumé:Cet article présente les résultats d`une recherche sur les préférences d’étudiants universitaires de français langue seconde en ce qui a trait aux différentes manipulations faites lors d’activités de grammaire informatisées. Dans un contexte d’activités à choix multiples, les étudiants ont indiqué leurs préférences envers les quatre manipulations proposées (cliquer, glisser, sélectionner dans un menu déroulant et dactylographier). Nos résultats, appuyés des commentaires des étudiants, démontrent en général que leurs préférences reflètent les caractéristiques des « enfants-roi » qui préfèrent des activités ludiques, faciles et rapides qui nécessitent peu d’investissement de leur part et ont un impact direct sur leurs notes. Abstract This article presents the results of a study on the preferences of French-as-a second-language university students towards different manipulations used in computerized grammar activities. Students indicated their preferences for the four manipulations offered (click, scroll-down menu, drag-and-drop, keyboard entry) while doing multiple-choice activities. Our results, backed up by student comments, show that their preferences reflect the traits of the “spoiled child” who prefers activities that are fun, easy and fast and that will have a direct impact on grades.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".