A Study on the Propensity to cheat in University Exams: Development and Validation Process of the Questionnaire on Cheating in University Exams (QCUE)
Bibliographic record
Abstract
This paper presents the seven-step approach to maximizing the evidence of validity that led to the development of the Questionnaire sur la tricherie aux examens à l’université (QTEU) [Questionnaire on Cheating in University Exams (QCUE)]. Composed of 28 items divided into 7 factors (propensity to cheat in exams, peer influence, cheating methods, institutional context, perception of control, performance goal, and commitment to one’s studies), the QCUE design was based on a comprehensive conceptual analysis of the scientific literature on cheating in exams, and on the work of Frenette, Hébert, Thibodeau, and Ndinga (2018) on how to develop a questionnaire maximizing the accumulation of validity evidence. With good psychometric properties, the QCUE meets a need for a French-language questionnaire on the propensity to cheat in exams and allows to measure the scope of cheating among university students.
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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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".