Engaging in Integrity: A Case Study on Leveraging the LMS for Faculty Education
Bibliographic record
Abstract
Expedited by the onset of the COVID-19 pandemic, the learning management system (LMS) has become a fixture in the infrastructure of the post-secondary classroom. This paper presents a case study describing the actions of a centralized Academic Integrity Office (AIO) at a Canadian community college that aimed to promote faculty engagement and support academic integrity education through the LMS. Specifically, we narrate the development and evolution of an LMS-based repository, examining its impacts and offering recommendations for enhancing social learning and community building. Over time, this repository was transformed into a more robust, centralized portal that improved access to academic integrity resources. Viewership increased to approximately 100 daily visitors, highlighting how platform selection influences access, which in turn supports faculty engagement and participation. This work seeks to address a gap in practice and scholarship by exploring how LMS functionalities and institutional portals can be leveraged to foster communication, build community engagement, and support the development of faculty and student academic integrity literacy while also strengthening faculty-practitioner partnerships.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.031 | 0.011 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".