How school districts influence student achievement
Bibliographic record
Abstract
Purpose The purpose of this paper is to test the effects of nine district characteristics on student achievement, explored the conditions that mediated the effects of such characteristics and contributed to understandings about the role school-level leaders play in district efforts to improve achievement. Design/methodology/approach Data for the study were provided by the responses of 2,324 school and district leaders in 45 school districts to two surveys. Student achievement evidence was provided by multi-grade provincial measures of math and language achievement. The analysis of these data included calculation of descriptive statistics, confirmatory factor analysis and regression mediation analysis. Findings Seven of nine district characteristics contributed significantly to student achievement and three conditions served as especially powerful mediators of such district effects. The same three conditions, as well as others, acted as significant mediators of school-level leader effects on achievement, as well. Practical implications District characteristics tested in the study provide a powerful framework for guiding the district improvement work of senior educational leaders. The organizational improvement efforts of both district and school leaders would be substantially enhanced by a better understanding of how to diagnose and improve the status of those conditions acting as significant mediators of the effects of both district and school leadership on student achievement. Originality/value This is one of a very few large-scale quantitative studies examining the extent to which characteristics frequently identified by district effectiveness research explain variation in student learning. It is also one of the very few studies identifying classroom, school and family variables that mediate district effects on such learning. The study also adds to a growing body of evidence about variables which mediate school leaders’ effects on such 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".