Promoting Academic Integrity: Utilizing Code of Conduct Statements with Students in an Online Course during the Pandemic
Bibliographic record
Abstract
Academic integrity is a fundamental principle that underpins the educational process, fostering a culture of trust, fairness, and ethical behavior in academic settings. It is crucial for universities to address these factors that influence cheating and create an environment that promotes academic integrity. This study explored an active approach to communicating the expectations of academic integrity in a virtual classroom. Participants in this study were enrolled in large sections of Introduction to Psychology courses at a midsize Canadian university. Observed grade inflation during the pandemic inspired the addition of a code of conduct statement to the course materials. The anticipated decrease in average exam scores after being exposed to the code of conduct did not occur. Findings indicate that having a code of conduct as a central document in an online setting is insufficient for inspiring academic honesty. We discuss reasons why this intervention was ineffective and provide recommendations for others wishing to use a code of conduct in an online course. We concluded, as have other researchers, that students’ attitudes toward cheating and peer-based norms are paramount to a culture of academic integrity.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".