Recommendations for a balanced approach to supporting academic integrity: perspectives from a survey of students, faculty, and tutors
Bibliographic record
Abstract
Abstract Maintaining academic integrity is a growing concern for higher education, increasingly so due to the pivot to remote learning in 2020 caused by the COVID-19 pandemic. We canvassed students, faculty, and tutors at an online Canadian university about their perspectives on academic integrity and misconduct. The survey asked how the university could improve policies concerning issues of academic integrity, how faculty and tutors handled cases of misconduct, about satisfaction with how academic violations were treated, and about the role of students, faculty, and tutors in encouraging academic integrity. As well, we collected suggestions from respondents for reducing cheating, addressing academic misconduct, and general ideas about academic integrity. The distinction between misconduct and integrity was not always clear in their comments. We received responses from 228 students and 73 faculty and tutors, generating hundreds of comments. In this paper we focus only on the answers to open-ended questions. Using content analysis, we categorized the replies into similar threads. After multiple iterations of analysis, we extracted three general recommendation groupings: Policy and Procedures, Compliance and Commitment, and Resources. Based on respondents’ views, we propose a balanced approach to supporting academic integrity. Although we conducted the study pre-COVID-19, the recommendations apply to current and future academic integrity practices in our context and beyond.
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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 | 0.099 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.006 | 0.007 |
| 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".