Bibliographic record
Abstract
Abstract- This paper discusses the evolution of a set of rubrics for the 12 CEAB graduate attributes in the Faculty of Engineering at the University of Manitoba. The rubrics are intended as a pedagogical assessment tool for instructors of individual courses as applicable, and for assessment at the program level. Individuals from faculty, industry and the University of Manitoba Centre for the Advancement of Teaching and Learning have been involved in the process of evaluating and revising both the content and wording of the rubrics in order that they meet the following criteria: (i) the foci and indicators adequately communicate the knowledge, skills, attitudes, values and behaviours that our engineering stakeholders agree do define each attribute; (ii) the competency level for each indicator is representative of what engineering educators and stakeholders agree defines proficiency; and (iii) the language in the rubrics is consistent and agreeable to all engineering stakeholders. These rubrics are expected to accomplish a number of outcomes-based pedagogical and accreditation goals, including: dividing the attributes into teachable and measurable foci and indicators; defining competency levels; and becoming a vehicle for the development of a common language for faculty, students and industry when they discuss, teach, assess and acquire the knowledge, skills and behaviours of the CEAB graduate attributes. This paper reports on the evolution of these rubrics, and outlines plans for their continued development and use within the faculty.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.077 | 0.020 |
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".