Coming Full Circle: What Happens When Your Class Turns into ‘Real Life’
Bibliographic record
Abstract
In this session, we will share some of the results of my experience with remote teaching and academic misconduct in my 200 seat, Introduction to Canadian Criminal Justice System class, which ran from May to Aug 2020 in the School of Criminology at Simon Fraser University. In June 2020, I learned quite by chance, that 41 of 200 students cheated on their midterm celebration of learning (ironically it was on the bonus question worth 1 of 90 marks - they looked up the due date of the quiz on academic integrity). After considerable thought, I decided to use course concepts and applied a restorative justice approach in my response and invited students to reach out and take responsibility. The student response was surprisingly encouraging, and I realized I needed to understand what happened more formally. Accordingly, we developed a study to examine student experiences with my response, how it affected their learning and understanding of course concepts and materials, and their feelings about academic integrity in online courses, especially during a global pandemic. My research assistant, Zana Nicolaou, and I will present findings from the 41 survey responses and 5 interviews that examined these questions. Then we will engage in conversation about how we can shift from conversations about academic misconduct to strategies that help us build a culture of academic integrity.
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.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.038 | 0.018 |
| Scholarly communication | 0.015 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".