Building a culture that values learning outcomes as an integral part of effective program development - one faculty’s example
Bibliographic record
Abstract
In 2016 and 2017, the Faculty of Engineering made significant efforts to review the state of all programs, and our course learning outcomes and redevelop them as necessary. This review was driven by new Canadian Engineering Accreditation Board (CEAB) requirements to report course learning outcomes as part of the Course Information Sheets. This paper looks at the work done in collaboration with the University’s Center for Teaching and Learning (CTL) in undertaking this initiative.Generally speaking, the experiences gained through this process were that learning outcomes benefit the instructors, students, and program alignment; regular workshops and one-on-one interactions improved the culture around learning outcomes amongst faculty members; having up-to-date learning outcomes must be a continuing process; learning outcomes are invaluable in ensuring continuity and consistency in course offerings; and, pedagogical/teaching service units are valuable partners in propagating pedagogical knowledge.
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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 | 0.039 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.018 | 0.015 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".