Effectiveness of Unproctored vs. Teacher-Proctored Exams in Reducing Students’ Cheating: A Double-Blind Randomized Controlled Field Experimental Study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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 it