Developing a university-wide academic integrity E-learning tutorial: a Canadian case
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it