How Stressed are Students and What Can We Do About It? Findings from a Self-report Survey of Contract Cheating Behaviours and the Stressful Events College Students Experience
Bibliographic record
Abstract
Empirical research on contract cheating in Canada has been limited (Eaton, 2022) and tends to focus on the university (Eaton, 2019; Stoesz & Los, 2019; Thacker, 2022) while there has been relatively little research on academic integrity and contract cheating in community colleges and other non-university higher education institutions (Bretag & Harper, 2020). To address this gap, in 2021, researchers collected data on student engagement in academic integrity violation behaviour and the stress they experienced as they were completing their programs at one Canadian community college. Using self-report survey methodology and utilizing students as partners in research, we found students engaged in a variety of contract cheating behaviours, and experienced a myriad of stressful events both in and outside the college context, including traumatic life events. In this presentation, we explore the link between stress and contract cheating behaviour and address how we can respond at all levels of our institutions to better support students and promote academic integrity.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".