Academic integrity: A restorative justice approach in first year engineering
Bibliographic record
Abstract
Academic integrity is a cornerstone of post-secondary education. Academic integrity violations disrupt the soundness of the assessment process, which is exacerbated in professional programs like engineering where accreditation hinges on the measurement of student proficiencies and graduate attributes. Engineering programs are typically challenging with demanding schedules and higher than typical workloads. Freshmen often face this challenge with deficient time management skills, which is coupled with increasing student mental health and wellness concerns. Combined with pressure to perform, these systemic issues can create circumstances in which students rationalize opportunities that constitute potential code of conduct violations, especially in group situations. The academic misconduct investigation process can be resource intensive, time intensive and stressful for students, instructors, and administrators. A restorative justice model was implemented as an alternative path to manage a large number of cases in first year engineering. The objective of using this approach was to educate students, emphasize the connection between academic integrity and engineering ethics and prevent further occurrences. This paper describes the development and use of this collaborative approach for first year.
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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 | 0.021 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.024 | 0.017 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.006 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| 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".