Bibliographic record
Abstract
With academic integrity anchored in teaching and learning (Bertram Gallant, 2016), perhaps its future could be positively influenced by more creative evaluation processes and methods. In this interactive presentation, members of Assiniboine Community College’s (ACC) Learning Commons share the value of designing and developing creative evaluations which maintain academic integrity in the evaluation process and align to college standards. With omnipresent concerns about academic misconduct spanning higher education, course and assessment design remain a way to prevent and reduce its occurrence through already established pedagogical strategies. The multidisciplinary team of ACC’s Library Manager, Education Quality Assurance Specialist, and Instructional Designer will facilitate an exploration of creative evaluation that can be achieved by using a constellation of approaches. This exploration is based primarily on the works of creative evaluation from Christou et al. (2021) and assessment for inclusion by Tai et al. (2022). Participants will leave with an understanding of what creative evaluations are and look like, and how to move towards designing and developing them at their own institutions.
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".