Helping teaching assistants build confidence and community through reciprocal peer observations: a no-budget, low-barrier approach
Bibliographic record
Abstract
Teaching assistants (TAs) are incredibly important in our teaching but are often under-supported in their roles-particularly in large courses, with large teams. Depending on instructor workload and TA contract hours, it can be challenging to support the professional development of these key members of the teaching team. To help our TAs develop their teaching and engage in a community of practice, we implemented a reciprocal peer observation. By observing each others' tutorials in a structured way, we promoted a sense of community and collaborative learning among the TAs with only a small investment of time by instructors. We structured these peer visits using COPUS-TA, an observational tool that guides their tutorial visit and feedback conversations. From this process, our TAs increased their sense of confidence and community. This is a relatively simple approach to give TAs professional development and connection to their peers, embedded directly within a large-enrollment undergraduate course.
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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 | 0.055 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".