Cheating after the test: who does it and how often?
Bibliographic record
Abstract
Self-reports suggest >50% of university students cheat at some point in their academic career (Christensen Hughes JM, McCabe DL. Can J High Educ 36: 49–63, 2006), although objective values of academic misconduct (AM) are difficult to obtain. In a physiology-based department, we had a concern that students were altering written tests and resubmitting them for higher grades; thereby compromising the integrity of our primary assessment style. Therefore, we directly quantified the prevalence of AM on written tests in 11 courses across the department. Three thousand six hundred and twenty midterms were scanned, and any midterm submitted for regrading was compared with its original for evidence of AM. Student characteristics, test details, and course information were recorded. On a department level, results show that this form of AM was rare: prevalent on 2.2% of all tests written. However, of the tests submitted for regrading, 17.4% contained AM (range: 0–26%). The majority of AM was conducted by high-achieving students, (60% of offenders earned >80%), and there was a trend toward women being more likely to commit AM ( P = 0.056). While our results objectively show that this type of AM is low, we highlight that large competitive courses face significantly higher prevalence, and high-achieving students may have gone underreported in previous literature. Vigilance should be employed by all faculty who accept tests for regrading.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".