Mathematics and academic integrity: institutional support at a Canadian college
Bibliographic record
Abstract
Academic integrity at our small Canadian college is informed by several key frameworks and centred around teaching, learning, and proactive education. Mathematics assessments were quickly moved through various learning environments over the past year, showing that some assessment design strategies were no longer feasible if they instead centred on potential academic misconduct. Other strategies commonly used in text-based fields, however, seem to have the potential for improving both academic integrity and learning in mathematics courses. While there are some legitimate uses for digital mathematics tools and apps in STEM fields and mathematics courses, students may use them, along with ‘homework help’ sites, for cognitive offloading. Connection to future careers at the assessment, course, program, and institutional levels will help students contextualize the importance of academic integrity. From the perspectives of students studying mathematics, wellness may be affected by the reactive or punitive use of academic misconduct identification methods such as e-proctoring. Instructional practices focused on increasing student self-efficacy and reducing mathematics anxiety may also help students to learn with integrity.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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; both teacher heads agree on what is shown here.
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".