Research-practice partnerships and communities of practice for fostering better teaching and learning
Bibliographic record
Abstract
We report on how a college and university physics-based research-practice partnership (RPP) has flourished in the greater Montreal area. The RPP has fostered bringing discipline content knowledge AND pedagogical knowledge together to design effective instructional innovations. It has co-designed activities and built resources that engage students meaningfully with the physics content (including for waves, optics, and modern physics), and provide evidence of statistically significant learning gains. This partnership has informed the development of a community of practice, Supporting Active Learning & Technological Innovation in Studies of Education (SALTISE), which facilitated the knowledge mobilization. SALTISE infrastructure allows us to share with other instructors, using a peer-to-peer professional development approach. These resources include: 1) a repository of peer-generated teaching and learning materials on the SALTISE website, 2) peer mentoring and support, 3) monthly virtual meetings and information sharing, and 4) a free annual conference that brings together practitioners and researchers. We discuss how this model could be useful for the SPIE community.
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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 | 0.102 | 0.103 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.026 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.006 | 0.044 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.023 | 0.004 |
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".