A Collaborative Process to Establishing PLOs at a Canadian University
Bibliographic record
Abstract
In 2007, the Ministers of Education across Canada adopted the Canadian Degree Qualifications Framework, articulating learning outcomes for bachelor’s, master’s, and doctoral degrees. Yet, by 2016, only 30% of Canadian institutions reported having learning outcomes for all programs (MacFarlane & Brumwell, 2016). One obstacle institutions face when developing program learning outcomes (PLOs) is faculty resistance. Unfortunately, faculty participation is critical to successfully implementing PLOs. This paper describes the process used to develop PLOs in the Faculty of Science at UBC Okanagan, which is deeply collaborative and consultative, to gain faculty buy-in and initiate a positive culture around learning outcomes and assessment. This was accomplished by educating faculty on the benefits and rationale for implementing PLOs, fostering faculty ownership of PLOs, supporting faculty through the process, and engaging with various stakeholders. This collaborative process led to community building, increased stakeholder commitment, laid the foundation for future collaborations, and fostered robust PLOs.
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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 | 0.064 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.044 | 0.017 |
| Scholarly communication | 0.015 | 0.007 |
| Open science | 0.005 | 0.027 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 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; 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".