Teaching Squares: Crossing New Borders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 it