COPUS-TA: An “Entry-Level” Peer Observation Tool to Support Teaching Assistant Professional Pedagogical Development
Bibliographic record
Abstract
When coordinating teams of teaching assistants (TAs) in our courses, it can be time and resource prohibitive to provide mentorship for individual professional development of their teaching. Peer observation of teaching is a useful and effective approach for professional development and for forming a community of practice that TAs can engage in. However, structured peer observation can require substantial training - which may make it infeasible for large teams of TAs with variable teaching expertise and limited contract hours. This article describes the development of an observation protocol, adapted for the TA context, from the Classroom Observation Protocol for Undergraduate STEM (COPUS; by Smith et al. 2013). We have used this successfully, with very minimal training (15-min discussion plus one practice observation). I include the modified form (COPUS-TA) for practical and immediate use in supporting the development of TAs' teaching skills.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".