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Record W2004698653 · doi:10.1080/13803611.2012.704171

An interdisciplinary analysis of microteaching evaluation forms: how peer feedback forms shape what constitutes “good teaching”

2012· article· en· W2004698653 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueEducational Research and Evaluation · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicTeacher Education and Leadership Studies
Canadian institutionsUniversity of TorontoMcMaster University
Fundersnot available
KeywordsMicroteachingMathematics educationClass (philosophy)Presentation (obstetrics)Teaching methodPeer feedbackQualitative researchQualitative propertyPsychologySample (material)Peer assessmentPerceptionSession (web analytics)PedagogyComputer scienceSociology

Abstract

fetched live from OpenAlex

Microteaching, a standard method for developing teaching skills, places high importance on peer feedback, which is guided by post-session feedback forms. This paper focuses on how feedback forms can shape what becomes understood as important to teaching. A sample of 10 microteaching evaluation forms drawn from North American postsecondary education institutions were examined using both qualitative and quantitative methods. Through a mixed-methods, interdisciplinary approach combining quantitative form classification based on distinct teaching elements with qualitative analysis drawing on Foucauldian and post-structural feminisms, key challenges are identified in the way that peer feedback forms may shape perceptions of what constitutes “good teaching”. We interpret that the close attention paid to the management of the body and the disproportionate focus on presentation and style may foreclose other modes of teaching beyond a conventional lecture-based class. This leads to a discussion on the ways in which the evaluation forms can be enhanced to provide a more effective educational tool.

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.

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)0.031
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0310.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.379
GPT teacher head0.571
Teacher spread0.192 · 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