18. Program-Level Assessment of Learning Outcomes in a Faculty Certificate Program on the Scholarship of Teaching and Learning
Bibliographic record
Abstract
105 O an international scale, higher education reform is having a profound impact on organizations and institutions, where there are now mandates and requirements to implement explicit learning outcomes and assessment policies for all undergraduate curricula (Bresciani, 2006; Hubball & Burt, 2004; Hubball & Burt, 2007). Program-level learning outcomes are a central component of learning-centred curricula and inform students what they can expect to achieve from a program of study so that they may organise their time and efforts, and prepare for assessments. They also connect segments of a curriculum, thus enhancing transferability of student learning, communicate curriculum/program goals in a meaningful way to a broader community, help to determine the extent to which learning has been accomplished, and guide faculty and administrators (within resource constraints), in part, to determine program(s) of study, course objectives, appropriate learning experiences, and assessment and program evaluation strategies (Barab & Duffy, 2000; Hubball & Gold, 2007). This paper highlights program-level assessment of learning outcomes in an 8-month Faculty Certificate Program (UBC-FCP) on the scholarship of teaching and learning (SoTL) at the University of British Columbia (UBC).
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".