THE ATTRIBUTE ASSESSMENT PROCESS IN THE FACULTY OF ENGINEERING AT THE UNIVERSITY OF MANITOBA: YEAR THREE
Bibliographic record
Abstract
This paper describes the third year of a studyat the University of Manitoba aimed at exploring how theCanadian Engineering Accreditation Board (CEAB)graduate attributes are manifested and measured in theFaculty of Engineering’s curriculum. Instructors from theDepartments of Biosystems, Civil, Electrical andComputer, and Mechanical Engineering were asked toconsider the presence of four attributes and theirsubsequent indicators in one engineering course taught inthe 2013-14 academic year. The attributes were: AKnowledge Base for Engineering, Individual and TeamWork, Impact of Engineering on Society and theEnvironment, and Economics and Project Management.Data were gathered using a self-administered checklist,which was introduced to instructors in a workshopsetting. The checklist has evolved over the three years inan effort to define student attribute competency levels andto create an assessment tool that meets the needs of boththe researchers and the instructors, as we work togetherto examine the graduate attributes in our courses andimplement an outcomes-based assessment protocol. Thedata from this third year give us the ability to report onhow the remaining four CEAB graduate attributes arepresently manifest and measured in our engineeringfaculty, to look for evidence of outcomes-basedassessment, to evaluate the checklist as an assessmenttool, and to reflect on the overall process.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".