RUBRICS AS A VEHICLE TO DEFINE THE TWELVE CEAB GRADUATE ATTRIBUTES, DETERMINE GRADUATE COMPETENCIES, AND DEVELOP A COMMON LANGUAGE FOR ENGINEERING STAKEHOLDERS
Bibliographic record
Abstract
This paper discusses the evolution of a set ofrubrics for the 12 CEAB graduate attributes in theFaculty of Engineering at the University of Manitoba. Therubrics are intended as a pedagogical assessment tool forinstructors of individual courses as applicable, and forassessment at the program level. Individuals from faculty,industry and the University of Manitoba Centre for theAdvancement of Teaching and Learning have beeninvolved in the process of evaluating and revising boththe content and wording of the rubrics in order that theymeet the following criteria: (i) the foci and indicatorsadequately communicate the knowledge, skills, attitudes,values and behaviours that our engineering stakeholdersagree do define each attribute; (ii) the competency levelfor each indicator is representative of what engineeringeducators and stakeholders agree defines proficiency;and (iii) the language in the rubrics is consistent andagreeable to all engineering stakeholders. These rubricsare expected to accomplish a number of outcomes-basedpedagogical and accreditation goals, including: dividingthe attributes into teachable and measurable foci andindicators; defining competency levels; and becoming avehicle for the development of a common language forfaculty, students and industry when they discuss, teach,assess and acquire the knowledge, skills and behavioursof the CEAB graduate attributes. This paper reports onthe evolution of these rubrics, and outlines plans for theircontinued development and use within the faculty.
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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 | 0.033 | 0.092 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.007 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.011 |
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".