Teaching Squares: Crossing New Borders
Bibliographic record
Abstract
Teaching Squares is a teaching development initiative that brings instructors together in small groups to observe one-another’s classes and reflect on their experiences in a non-judgmental, supportive environment (University of Waterloo, (n.d.). Durham College, the University of Ontario Institute of Technology (UOIT), and a key industry partner, Ontario Power Generation (OPG), have partnered on a Teaching Squares initiative, enabling primarily face-to-face discussions amongst instructors at all three institutions. Despite positive feedback and minimal time demands, building faculty enrollment and involvement remains challenging to engage instructors across various disciplines, fields, and delivery formats. In the fall 2017 semester, a professor teaching in a fully online program enrolled in Teaching Squares, participating completely online. Although the significance of peer observation to support teaching in an online environment is well documented (Bennett & Santy, 2009; Swinglehurst, et al., 2008), there were logistical challenges, including arranging recordings of face-to-face classes for the online professor to observe, and involving the professor in face-to-face discussions amongst program participants. Despite the challenges, this experience inspired discussion about how Teaching Squares may be piloted in a fully online format. This paper and presentation will continue this discussion, extending it to the possibilities of expanding enrollment to international partners to promote the exchange of ideas across institutional and geographical borders and to provide more diversity of perspectives on Teaching and Learning in a digital context.
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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 | 0.014 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.014 |
| Scholarly communication | 0.015 | 0.023 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 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".