The Factors of Contributing to Adademic Misconduct of Engineering Students Survey: Preliminary Results
Bibliographic record
Abstract
Academic misconduct is a serious problem in engineering higher education as it undermines student learning as well as the credibly of higher education institutions. The Factors contributing to Academic Misconduct of Engineering Students (FAMES) is a student survey developed to examine students’ perceptions on academic misconduct behaviour and to identify factors that predict self-reported cheating on proctored and unproctored assessments. The survey has been distributed at two Canadian universities. Preliminary results indicate that while students indicate they perceive some academic violations as inappropriate, other behaviours are not reported to be problematic. Students’ attitudes toward misconduct also depends on the assessment type. In addition, the less serious students consider an attitude, the more frequent they reported to engage in that behaviour. As has been reported elsewhere, students perceive academic misconduct among their peers happens at a much higher rate than what is indicated by aggregate self-reports. Future data analysis will include examining which factors related to the Fraud Triangle theory influence students’ decisions to engage in academic misconduct.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".