Improving the Capacity of School System Leaders and Teachers to Design Productive Learning Environments
Bibliographic record
Abstract
In this article we report on the results of an innovative research partnership with the largest school district in one Canadian province where we are exploring how educational leadership practices and the factors that influence these practices interact to impact student learning. This article makes a clear connection between leadership and student learning and makes a significant contribution to the knowledge base regarding what we know about leadership in educational contexts, how and how much leadership matters within that context, as well as how important those effects are in designing productive learning environments that facilitate the learning of all children. Using Arbuckle's Amos 17 and maximum likelihood estimation, we employed path analysis procedures to develop a best-fitting nested model to examine the interrelationships among three primary sources of formal leadership for education found in schools, school districts, and government, and how these leaders interact with one another and with professional teachers, parents, and other community stakeholders to directly and indirectly impact the existence of a clear focus on student learning. We conclude with a discussion of the pathways in our best-fitting model as we explore in detail the interrelationships among the primary sources of leadership and discuss the direct and indirect effects of each of these leadership sources on one another and on the extent to which the factors individually and collectively impact a school's focus on student 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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".