Institutional Approaches to Evaluate Teaching Effectiveness: The Role of Summative Peer Review of Teaching for Promotion and Tenure
Bibliographic record
Abstract
A growing body of literature has identified student evaluations of teaching (SETs) as introducing bias against minority faculty members and not serving as a reliable or valid measure of teaching effectiveness. This lack of reliability and validity presents issues for university tenure and promotion committees, as these institutional processes necessarily require accurate, objective, and holistically informed modes of evaluation to recognize teaching achievements. Summative peer review of teaching (SPRT) is an alternative mode of assessment that aims to provide evidence of teaching effectiveness to inform promotion and tenure. SPRT, as an institutional practice, has been adopted at a small cohort of institutions of higher education, marking a potential shift in practice. This article examines SETs to articulate the problematic elements introduced by SETs, specifically to examine if SPRT can serve as a viable alternative. By describing the SPRT processes that four institutions have taken, the authors aim to articulate these emerging approaches to collecting evidence of teaching effectiveness. In this descriptive work, it is our secondary contention that SPRT, through intentional design and facilitation, can offer a process that does not introduce bias in the same way as SETs and thus, can also be used to satisfy the growing need for practices that help achieve, in part, institutional goals related to equity, diversity, and inclusion (EDI).
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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 | 0.537 | 0.686 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.025 | 0.015 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.002 | 0.005 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".