MétaCan
Menu
Back to cohort
Record W1513672721 · doi:10.21432/t2xs3f

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

2010· article· fr· W1513672721 on OpenAlexaffvenue
Nandini Sarma, Alysse Weinberg, Martine Peters

Bibliographic record

VenueCanadian Journal of Learning and Technology · 2010
Typearticle
Languagefr
FieldArts and Humanities
TopicLinguistics and Discourse Analysis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsHumanitiesArtPsychology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.651
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.015
GPT teacher head0.256
Teacher spread0.241 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of Learning and TechnologySame topicLinguistics and Discourse AnalysisFrench-language works237,207