Student perceptions of academic misconduct amongst their peers during the rapid transition to remote instruction
Bibliographic record
Abstract
Abstract The sudden move from traditional face-to-face teaching and learning to unfamiliar virtual spaces during the early weeks and months of the COVID-19 pandemic demanded many members of educational communities around the world to be flexible and teach and learn outside of their comfort zones. The abruptness of this transition contributed to instructors’ concerns about academic cheating as they could no longer assess learning and monitor student progress using their usual strategies and methods. Students also experienced disruptions to their usual ways of learning, which may have contributed to poor decision-making, including engagement in academic misconduct. The present study examined students’ beliefs about increased engagement in academic misconduct by their peers during the rapid obligatory transition to remote instruction due to the COVID-19 pandemic in March 2020. In January 2021, a retrospective online survey was distributed to students in undergraduate courses. We focused our analyses of the responses from students at a single university in Canada. We found that beliefs of increased cheating depended upon student gender (men vs women), status (domestic vs international), year of study (Years 1/2 vs Years 3 +), and discipline (Science, Technology, Engineering, and Mathematics vs Social Sciences and Humanities). These are important findings as they provide insight into the nature of the culture of academic integrity during a stressful and confusing period in postsecondary students’ lives.
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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 | 0.004 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".