Investigating how autonomy‐supportive teaching moderates the relation between student honesty and premeditated cheating
Bibliographic record
Abstract
BACKGROUND: Cheating at the post-secondary level is a skewed phenomenon. While personality and environmental factors are associated with cheating, few studies account for the zero inflation when predicting cheating behaviour. AIM: In this study, we explore a person-situation interaction hypothesis where teacher autonomy support (AS) could modify the relation between students' honesty trait and premeditated cheating. SAMPLE: Participants were 710 college students and 31 teachers. METHODS: Teacher and student reports of teacher AS were collected and students also completed self-reports of honesty and premeditated cheating. RESULTS: Given that cheating had a zero-inflated negative binomial distribution, we can investigate two separate outcomes: likelihood of cheating and magnitude of cheating. Predictably, student honesty trait predicted lower likelihood and magnitude of cheating. AS, whether student- or teacher-reported, moderated the relation between honesty and likelihood of cheating. In low perceived AS teaching environments, student honesty was associated with cheating likelihood. However, there was no such relation in high perceived AS teaching environments. CONCLUSIONS: Students' honesty generally predicts lower cheating. However, the educational environment provided by the teacher influences the strength of this association. The less autonomy-supportive students perceive the educational environment, the more their personality is important in predicting the likelihood of cheating.
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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 | 0.003 | 0.017 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".