Educating College Students on Academic Integrity: What Roles do Teachers Play in the Quebec Province?
Bibliographic record
Abstract
Over the past 15 years, there has been a shift from detecting and sanctioning academic misconduct to preventing and promoting desired academic practices (Ferguson et al., 2007). According to Gray and Jordan (2012), teachers represent an influential figure to enhance students’ understanding of academic standards. However, a study conducted by Löfström et al. (2014) reveals that educators do not share the same perspectives on how academic integrity should be taught and whose responsibility it is to teach it. Moreover, Peters et al. (2019) developed a conceptual framework illustrating seven roles adopted by educators when teaching academic integrity, with each end of the spectrum identified as acting as “Ambassador” to being completely “Detached” (p. 6). These two teams of scholars examined the teaching of academic integrity at the university level, but little is known about how this is taught at the college level. This is why we explored the situation in seven colleges[1] across the province of Quebec in Canada. During our communication, we will present results from semi-structured interviews with 17 college teachers. A discussion will follow on winning strategies utilized by college teachers for promoting academic integrity among the student population. Finally, we will recommend further avenues of exploration in the domain of teaching and learning at institutions of higher education to foster a culture of academic integrity. [1] In the Quebec province, colleges are called cégep (collège d’enseignement general et professionnel) to prepare students for university studies or provide them with technical skills to enter the labour market.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".