How to Talk About Academic Integrity so Students Will Listen: Addressing Ethical Decision-Making Using Scenarios
Bibliographic record
Abstract
Abstract The field of academic integrity in higher education has made significant gains in exploring the proliferation of integrity issues, the frequency of student misconduct behaviours, and in identifying strategies for embedding academic integrity education more broadly into the curriculum. Regardless of calls for institution-wide approaches which focus on preventing academic misconduct, those of us engaged in the field can attest that there will always be a need to address academic misconduct behaviours and support the development of those students who engage in them. As student affairs practitioners in a Canadian post-secondary institution, we present our approach to creating meaningful teaching and learning experiences that enable students with misconduct violations to critically explore potential misconduct situations and practice the skills needed to make alternative decisions. Utilising existing work that frames academic integrity as ‘standards of practice’, this chapter demonstrates our application of key themes from the academic integrity literature within our teaching and learning practice. Recognizing that mandated academic integrity education can be a challenging learning experience, we discuss our approach to engaging these students in analyzing the common situational factors that post-secondary students face that pose potential academic integrity conflicts and the way ethical decision-making frameworks can support their ability to navigate academic integrity concerns in the future. We conclude the chapter with our key learnings and recommendations for implementing an engaging experience with students who are mandated to attend instruction following an academic integrity violation.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".