Teacher-Directed Collaborative Action Research as a Mediating Tool for Professional Learning in Rural Contexts
Bibliographic record
Abstract
Through a single-case study design, the research study described in this article examined one rural Canadian school division’s use of teacher-directed collaborative action research as a mediating tool for teacher learning within a professional development (PD) initiative known as the Numeracy Cohort. The PD initiative brought together a dozen K-12 teachers from across a very small (but geographically distanced) school division in Manitoba, Canada. In addition to learning about several strategies for teaching mathematics and improving student numeracy skills, the teachers in the Numeracy Cohort engaged in collaborative action research projects, designing materials and implementing new strategies in their unique, often multi-grade, rural classrooms. In addition to the changes and improvements noticed by teachers through their collaborative action research, findings from the study illustrated several strengths of teacher-directed collaborative action research, including the autonomy it afforded teachers to engage in work directly related to their classroom contexts, its ability to foster collaboration between colleagues, and its ability to build connections across schools within a diverse rural context. Findings from the study also suggested that consideration should be given to both ways of supporting the action research process, and the complexities of leadership in rural settings if teacher-directed collaborative action research is to be used as a mediating tool for learning.
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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 | 0.051 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.017 | 0.018 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".