Linking Leadership to Student Learning: The Contributions of Leader Efficacy
Bibliographic record
Abstract
Purposes: This study aimed to improve our understanding of the nature, causes and consequence of school leader efficacy, including indirect influences on student learning. We asked about district contributions to school leader efficacy, whether leader self- and collective efficacy responded to the same or different district conditions and the effects of leader efficacy on conditions in the school and the learning of students. Methods: Evidence for the study was provided by 96 principal and 2,764 teacher respondents to two separate surveys, along with student achievement data in language and math averaged over 3 years. Path analytic techniques were used to address the objectives for the study. Findings: In this study, school leaders' collective efficacy was an important link between district conditions and both the conditions found in schools and their effects on student achievement. School leaders'sense of collective efficacy also had a strong, positive, relationship with leadership practices found to be effective in earlier studies. Implications: These results suggest that district leaders are most likely to build the confidence and sense of collective efficacy among principals by emphasizing the priority they attach to achievement and instruction, providing targeted and phased focus for school improvement efforts and by building cooperative working relationships with schools.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".