An interdisciplinary analysis of microteaching evaluation forms: how peer feedback forms shape what constitutes “good teaching”
Bibliographic record
Abstract
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.
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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 | 0.062 | 0.186 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".