Making Teaching Communal: Peer Mentoring through Teaching Squares
Bibliographic record
Abstract
Teaching can often seem like an independent endeavor, and seeking out ways to engage in dialogue and exchanges surrounding teaching can be beneficial. Opportunities to observe peers’ teaching and discuss teaching practices, challenges, and experiences with peers can lead to an increased sense of community, a fruitful exchange of ideas, and ultimately more thoughtful and effective teaching (Hendry and Oliver, 2012; Lemus-Martinez et al., 2021). One venue for such engagement is the teaching square, an exercise in which teachers observe each other’s teaching practice, typically with the goal of self-reflection of one’s own practice rather than evaluation of a peer performance. We suggest that even as the common philosophy behind teaching squares emphasizes self-reflection, they can also be catalysts for peer mentoring among participants. This article discusses teaching squares as a peer mentorship opportunity, drawing attention to the benefits of cultivating peer mentorship focused on teaching practices. We provide an account of our experience in undertaking a teaching square and the informal peer mentorship that resulted.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".