Inter-Institutional Collaboration for the Development of a Local Peer Observation Process to Enhance Teaching
Bibliographic record
Abstract
Local peer observation of teaching is considered an important mechanism for instructors to improve the quality and effectiveness of their teaching, but there is an absence of uniformity to establish a best practice for this process in veterinary curricula. The Regional Teaching Academy (RTA) of the Consortium of Western Colleges of Veterinary Medicine is comprised of educational advocates from five western veterinary colleges with a common goal of enhancing the quality and effectiveness of education in veterinary medical curricula. Members of the RTA recognized this deficit in best practices for local peer observation (LPO) and formed a working group called "Local Peer Observation of Teaching." The goal was to meet a critical need for the enhancement of individual teaching skills by using a scholarly approach to develop robust methods for peer observation of teaching. Two rubric-based instruments were developed: one for large-group/didactic settings, and the second for small-group/clinical settings. Each is accompanied by pre- and post-observation worksheets which are considered instrumental to success. Results of a qualitative survey of instrument users' experiences are shared. Both observers and observees view the experiential learning from faculty peer colleagues very positively and the meaningful feedback is appreciated and incorporated by observees. Suggestions for implementation of the peer observation process are discussed, considering strengths and challenges. The purpose of this article is to describe in depth, the development process and output of the efforts of the Local Peer Observation of Teaching working group as a potential best practice guideline for peer observation.
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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 | 0.145 | 0.150 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.005 | 0.021 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".