Fostering Scholarly Approaches to Peer Review of Teaching in a Research-Intensive University
Bibliographic record
Abstract
This chapter examines a recently launched institutional initiative around scholarly approaches to summative and formative peer-review of teaching within and across the disciplines at the University of British Columbia (UBC), Canada. The peer-review of teaching initiative, led by a team of UBC national teaching fellows, was fuelled by institutional concerns about the quality of student learning experiences and the effectiveness of teaching in a multi-disciplinary research-intensive university context. Canadian universities have long recognized the importance of attending to the evaluation of teaching practices in their particular context; however, the enactment of localized scholarship directed at these practices remains very much in its infancy. Traditional approaches to the evaluation of university teaching have often resulted in the over-reliance on student evaluation of teaching data and/or ad-hoc peer-review of teaching practices with numerous accounts of methodological shortcomings that tend to yield less useful (and less authentic) data. Issues addressed in this chapter include contemporary approaches to the evaluation of teaching in higher education, faculty “buy-in” and the evaluation of teaching in a research-intensive university, scholarly approaches to summative and formative Performance Reviews of Teaching (PRT), faculty-specific engagement in summative and formative (informal to formal) PRT training and implementation, and strategic institutional supports (funding, expertise, mentoring, technological resources).
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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 | 0.041 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".