Data-Informed Participatory Leadership: Cultivating Collective Decision-Making in K–12 Schools Through Global Research and Local Practice
Bibliographic record
Abstract
This presentation explores a research-based framework for participatory leadership in K–12 school systems, emphasizing the role of data-informed decision-making in fostering inclusive and sustainable school improvement. Drawing on foundational theories—transformational leadership, distributed leadership, and evidence-based management—it highlights how collaborative governance structures enhance educational outcomes, teacher engagement, and community trust. Global case studies from Finland, Canada, and the United States are examined, alongside practical insights from higher education leadership at Pepperdine University. The presentation introduces the “School-Based Participatory Data Teams” (SBPDT) model as a scalable approach for integrating collective voice and empirical evidence into school decision-making. The methodology is grounded in action research and design-based research, ensuring adaptability to local cultural and institutional contexts. This work aims to inspire Iranian educators and school leaders to adopt data-informed participatory strategies that elevate both teaching and 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 | 0.046 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".