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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".