Developing a university-wide academic integrity E-learning tutorial: a Canadian case
Bibliographic record
Abstract
Abstract Academic integrity has become a significant point of concern in the post-secondary landscape, and many institutions are now exploring ways on how to implement academic integrity training for students. This paper delineates the development of an Academic Integrity E-Learning (AIE-L) tutorial at MacEwan University, Canada. In its first incarnation, the AIE-L tutorial was intended as an education tool for students who had been found to violate the University’s Academic Integrity Policy. However, in a discourse of the academic integrity process, the University reimagined it from only emphasising the increased understanding and strengthened commitment of students found to have committed academic misconduct to a proactive focus with education for all students. The purpose of the present paper is three-fold: first, describe the development of the AIE-L tutorial as an experiential case study; second, improve the content of the AIE-L tutorial through students’ quantitative and qualitative feedback; third, calibrate the pre and post-test questions for content validity for a forthcoming large-scale measurement of the AIE-L tutorial effectiveness.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.004 | 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".