CO-OP EMPLOYER EVALUATION OF THE GRADUATE ATTRIBUTES: A COMPARISON OF TWO APPROACHES
Bibliographic record
Abstract
Cooperative education (co-op) is animportant and mandatory part of undergraduateengineering at the University of Victoria (UVic). Becauseof this close integration with the curriculum, the Facultyof Engineering has chosen to use co-op employerevaluations of students as part of the assessment of theCEAB graduate attributes. This paper will describe thetwo employer surveys currently in use at UVic: oneadministered by the university’s co-op office andrepurposed for attribute assessment; and a second,possibly unique in Canada, designed expressly foremployer assessment of the attributes. Results arepresented from each, showing our employers tend to rankstudents highly in attributes such as Knowledge Base,Ethics and Equity, and Life-Long Learning, but lower inEconomics and Project Management. When results fromthe two surveys are combined, we find systematicdifferences between the responses from the two tools. Weconclude that caution is needed when combining resultsfrom different assessment tools.
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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 | 0.031 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".