How do faculty members respond to their students' discussions of academic misconduct and academic integrity?
Bibliographic record
Abstract
The present study conducted a qualitative analysis of faculty members? perceptions, beliefs and instructional concerns regarding academic integrity in their classrooms following their observation of their students engaged in a 45-minute interactive presentation on academic integrity. Overall, seven overarching themes and a series of sub-themes were identified including the following: comfort level and knowledge about academic integrity issues (for faculty and for students), impressions about the interactive presentation, student engagement in the presentations, learning outcomes for faculty, safeguards against misconduct, and issues, consequences and proposed solutions to concerns. Key findings within these themes suggest that faculty members perceived themselves to be confident in their own understanding of what constitutes academic integrity; however, there were inconsistencies regarding whether their students had the requisite knowledge to make appropriate decisions. Faculty members were surprised by the frank and engaged interactions of their students during the interactive presentations. Only half of the faculty found the presentation content enhanced their own current knowledge. Faculty identified several methods they use to safeguard against academic misconduct, and identified the importance of both faculty and the institution providing a consistent and clear model to promote academic integrity in students. Discussion explores insights gained as a context for informing instructional practice.
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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 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".