THE FACULTY OF ENGINEERING ATTRIBUTE ASSESSMENT PROCESS AT THE UNIVERSITY OF MANITOBA: SUGGESTIONS FOR CLOSING THE LOOP
Bibliographic record
Abstract
This paper describes the findings from athree-year longitudinal study at the University ofManitoba designed to explore how the CanadianEngineering Accreditation Board (CEAB) graduateattributes are manifested and measured in the Faculty ofEngineering’s curriculum. Instructors from theDepartments of Biosystems, Civil, Mechanical, andElectrical and Computer Engineering were asked toconsider the presence of four of the 12 CEAB attributesand their subsequent indicators in one engineering coursetaught in one academic year. Each year, four differentattributes were targeted, chosen to reflect both thetraditional/technical and the professional/workplacecompetencies. Data were collected using a selfadministeredchecklist, which evolved over the three yearsof the study in an effort to more clearly define studentattribute competency levels, and to develop a commonlanguage and understanding in regards to the graduateattributes and the process of outcomes-based assessment.This final phase of the study enables us to understand howall 12 of the CEAB graduate attributes are manifest andmeasured across our engineering curricula, to discussour findings within the context of outcomes-basedassessment and accreditation protocols, and to strategizeways to close the loop.
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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 | 0.231 | 0.294 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.025 | 0.025 |
| Open science | 0.013 | 0.014 |
| Research integrity | 0.014 | 0.022 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier 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".