A Literature Review on the Culture of Cheating in Undergraduate Engineering Programs
Bibliographic record
Abstract
Anecdotally, cheating is perceived to happen in all Canadian engineering programs in varying degrees. The authors of this study want to understand the cultures of cheating in the Engineering Colleges at the Universities of Saskatchewan and Regina, to inform efforts to reduce the prevalence of cheating. The first step that has been undertaken in this process is a literature review of previous studies on the general topic of cheating in undergraduate engineering programs.As it happens, virtually all of these studies have taken place in the United States, further motivating parallel work here in Canada. Surveys have recently been distributed to students and faculty at the Universities of Saskatchewan and Regina, where the content of those surveys has been strongly influenced by high-quality work carried out by American researchers of this topic.In this paper, we will describe the research work that has been performed previously, and the survey and measurement tools that have been utilized in past studies e.g. PACES-1, PACES-2 and SEED. The general and specific methods that have been employed will be described, and the results will be summarized. For example, it is known that faculty and students often have very different definitions of, and beliefs around, cheating. In practical terms, this manifests itself in the differing attributions of responsibility for cheating.We conclude our paper by constructing a concise framework that summarizes the current understandings of how cheating is defined in an academic context for engineering, the most common ethical footings that underlie those definitions, and the conditional behaviours that result from them. Finally, we speculate on the potential differences that may arise in a Canadian context, and we describe the approach that we have taken to studying cheating at our own institutions using surveys and other evaluative processes.
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.026 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".