Testing a Conception of How School Leadership Influences Student Learning
Bibliographic record
Abstract
Purpose: This article describes and reports the results of testing a new conception of how leadership influences student learning (“The Four Paths”). Framework: Leadership influence is conceptualized as flowing along four paths (Rational, Emotions, Organizational, and Family) toward student learning. Each path is populated by multiple variables with more or less powerful effects on student learning. Leaders increase student learning by improving the condition or status of selected variables on the Paths. Research Methods: Evidence includes teacher responses to an online survey (1,445 responses) measuring distributed leadership practices in their schools ( N = 199) and variables mediating leaders’ effects on students. Grade 3 and 6 math and literacy achievement data were provided by the province’s annual testing program. The 2006 Canadian Census data provided a composite measure of school socioeconomic status. Path modeling techniques were used to test six hypotheses. Results: The Four Paths model as a whole explains 43% of the variation in student achievement. Variables on the Rational, Emotions, and Family Paths explain similarly significant amounts of that variation. Variables on the Organizational Path were unrelated to student achievement. Leadership had its greatest influence on the Organizational Path and least influence on the Family Path. Implications: School leaders and leadership researchers should be guided much more directly by existing evidence about school, classroom, and family variables with powerful effects on student learning as they make their school improvement and research design decisions.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".