A Pre-Registered Examination of the Relationship Between Personality, Stress, and Academic Cheating in the Age of Online Learning
Bibliographic record
Abstract
The shift to online learning during the COVID-19 pandemic provided university students with many more opportunities for academic cheating. Using survey data from 530 Canadian undergraduate students collected during the winter semester of 2021, we examined the relationships between stress due to COVID-19, attitudes, personality traits (i.e., HEXACO, psychopathy, grandiose and vulnerable narcissism, Machiavellianism), demographic variables, and engagement in academic cheating during the fall 2020 semester. Cheating was assessed using both a binary self-report (yes/no) and a checklist of 14 specific cheating behaviours. Overall, 67.5% of students admitted to engaging in at least one form of cheating (e.g., using textbooks during online exams), and 86.6% believed that moving classes online increased cheating rates among other students. Regression analyses indicated that younger age, positive attitudes toward cheating, and lower honesty-humility (e.g., dishonest, greedy, immodest) were associated with higher cheating engagement across both measures. On the other hand, grandiose narcissism was uniquely related to the behavioural checklist, whereas vulnerable narcissism was uniquely related to the binary cheating outcome. Contrary to our expectations, COVID-related stress was unrelated to cheating. Considering our findings, we discuss avenues for targeted interventions that may help promote academic integrity in current university settings.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".