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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".