Effectiveness of Unproctored vs. Teacher-Proctored Exams in Reducing Students’ Cheating: A Double-Blind Randomized Controlled Field Experimental Study
Bibliographic record
Abstract
Unproctored and teacher-proctored exams have been widely used to prevent cheating at many universities worldwide. However, no empirical studies have directly compared their effectiveness in promoting academic integrity in actual exams. To address this significant gap, in four preregistered field studies, we examined the effectiveness of unproctored and teacher-proctored exam formats in deterring cheating behavior among university students and the role of academic integrity reminders. All four studies used a double-blind, randomized, controlled design. Before taking an exam, students were randomly assigned to take either an unproctored condition or a teacher-proctored exam, with or without receiving an academic integrity reminder. We found that the unproctored exam format is significantly more effective in reducing cheating than the teacher-proctored exam format and adding academic integrity reminders before the exams significantly reduces cheating. These findings demonstrate that incorporating unproctored exams and pre-exam academic integrity reminders into a university’s assessment practices may be a useful strategy for reducing academic dishonesty and upholding assessment validity.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".