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Record W4316589303 · doi:10.7202/1095479ar

Institutional Approaches to Evaluate Teaching Effectiveness: The Role of Summative Peer Review of Teaching for Promotion and Tenure

2023· article· en· W4316589303 on OpenAlexaffvenue
Keif Godbout-Kinney, Gavan Watson

Bibliographic record

VenueCanadian Journal of Educational Administration and Policy · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicEvaluation of Teaching Practices
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsSummative assessmentPromotion (chess)Equity (law)Inclusion (mineral)Diversity (politics)Process (computing)RedressFormative assessmentMathematics educationPsychologyPublic relationsMedical educationComputer sciencePedagogySociologyPolitical scienceSocial psychologyMedicine

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.021
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.748
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.021
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.248
GPT teacher head0.477
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2023
Admission routes2
Has abstractyes

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