Research Practice Partnerships: A Cultural-Historical Activity Theory Analysis of Teacher Professional Learning, Agency, and Identity
Bibliographic record
Abstract
Research Practice Partnerships (RPPs) are an emerging practice in Alberta, Canada, and internationally. By employing design-based research (DBR) methods, researchers and teacher practitioners in RPPs collaborate to address messy problems of practice in naturalized settings. Ideally, the design and enactment of partner research initiatives leads to a reciprocal and mutually-beneficial learning process for researchers and participants leading to improved practice and informed theory. However, there can be challenges to creating effective research partnerships. RPPs can be considered transformative interventions for expansive learning. Through the application of the Cultural-Historical Activity Theory (CHAT) framework, this research informs the design and enactment of RPPs by developing a deeper understanding of how professionals learned within a research partnership and how their professional agency and identity were impacted through participation. By employing case study as a methodology, this study developed thick descriptions of teacher professional learning, agency, and identity within a Partner Research Schools initiative between a University, professional learning organization, and a large urban school district. The Cultural-Historical Activity Theory (CHAT) framework was employed as an analytical tool to understand the impact of the RPP on professional practice.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".