Étude sur la propension à tricher aux examens à l’université : élaboration et processus de validation du Questionnaire sur la tricherie aux examens à l’université (QTEU)
Bibliographic record
Abstract
Cet article présente la démarche en sept étapes visant à maximiser l’obtention de preuves de validité qui a mené à l’élaboration duQuestionnaire sur la tricherie aux examens à l’université(QTEU). Composé de 28 énoncés répartis sous 7 facteurs (propension à tricher aux examens, influence des pairs, modalités pour tricher, contexte institutionnel, perception de contrôle, but de performance et engagement dans les études), le QTEU a été conçu en prenant appui sur une analyse conceptuelle approfondie de la littérature scientifique sur la tricherie aux examens et sur les travaux de Frenette, Hébert, Thibodeau et Ndinga (2018) sur la manière d’élaborer un questionnaire présentant diverses preuves de validité. Grâce à ses qualités psychométriques acceptables, le QTEU vient combler un besoin de questionnaire de langue française sur la propension à tricher aux examens et permet de mesurer son étendue auprès des étudiants universitaires.
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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 | 0.074 | 0.145 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".