Development of a Formative Assessment Rubric for Peer Evaluation of Teaching (FARPET) and Pilot Use in Veterinary Online Teaching
Bibliographic record
Abstract
Peer evaluation of teaching (PET) serves an important role as a component of faculty development in the medical education field. With the emergence of COVID-19, the authors recognized the need for a flexible tool that could be used for a variety of lecture formats, including virtual instruction, and that could provide a framework for consistent and meaningful PET feedback. This teaching tip describes the creation and pilot use of a PET rubric, which includes six fixed core items (lesson structure, content organization, audiovisual facilitation, concept development, enthusiasm, and relevance) and items to be assessed separately for asynchronous lectures (cognitive engagement-asynchronous) and synchronous lectures (cognitive engagement-synchronous, discourse quality, collaborative learning, and check for understanding). The instrument packet comprises the rubric, instructions for use, definitions, and examples of each item, plus three training videos for users to compare with authors' consensus training scores; these serve as frame-of-reference training. The instrument was piloted among veterinary educators, and feedback was sought in a focus group setting. The instrument was well received, and training and use required a minimum time commitment. Inter-rater reliability within 1 Likert scale point (adjacent agreement) was assessed for each of the training videos, and consistency of scoring was demonstrated between focus group members using percent agreement (0.82, 0.85, 0.88) and between focus members and the authors' consensus training scores (all videos: 0.91). This instrument may serve as a helpful resource for institutions looking for a framework for PET. We intend to continually adjust the instrument in response to feedback from wider use.
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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 | 0.157 | 0.253 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".