Contract Cheating: An Inter-Institutional Collaborative SoTL Project from Alberta
Bibliographic record
Abstract
Our project showcases perspectives from three Alberta post-secondary institutions, using a collaborative action research approach to reflect upon and then develop interventions to advance awareness of, and responses to, contract cheating. Contract cheating includes, but is not limited to essay mills, custom writing services, assignment completion services and professional exam takers. Contract cheating also occurs when parents, partners or another student do the work for a learner. In short, contract cheating happens when students have someone else complete academic work on their behalf, but submit their work as if they had done it themselves. Our project is framed as an action research project that extends SoTL beyond the individual classroom to a broader institutional context. Using narratives, observations and reflection-on-action as data sources, we use informal interventions such as hallway conversations and in-class discussions, designed to help both faculty members and students develop greater awareness about what contract cheating is and why it deserves attention from a teaching and learning perspective. We also discuss institutional action, such as taking part in the International Day of Action Against Contract Cheating, as a formal way to raise awareness about contract cheating in higher education more broadly. We conclude with preliminary practical and evidence-informed recommendations for practitioners, educational developers and decision-makers.
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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 | 0.018 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.030 | 0.016 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.003 | 0.004 |
| 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".