Intersecting Roadmaps: Resolving Tension Between Profession-Specific and University-Wide Graduate Attributes
Bibliographic record
Abstract
Can we map university-wide graduate attributes to specific program requirements? Can we develop and manage an integrated assessment process? In this article, we present a seven-month long project where we attempted to map generic university graduate attributes (UGAs) to required engineering program graduate attributes in a large Canadian research institution. The purpose of the project was to explore the intersection of the UGAs with engineering graduate attributes, evaluate the accreditation process, develop a mapping process, and examine management strategies for assessing both sets of graduate attributes, all the while keeping the continual improvement process attractive to students, instructors, and administrators. Using a modified dialectical inquiry, two groups worked on the mapping process: one from engineering, the other from social sciences (Education and Arts), to ensure objectivity of comparison. Both forward and backward mapping took place. Results demonstrated that, although generic, UGAs may not necessarily capture specific professional program graduate attributes. The study also highlighted the need for more revisions and updates of UGAs by including various stakeholders who can substantially contribute to the implementation and assessment of UGAs.
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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 | 0.047 | 0.121 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".