Principal's Instructional Leadership: Leading Capacity for Learning
Bibliographic record
Abstract
Over the past decades, a body of literature on the instructional role of the principal has emerged (Hallinger , 1996). In our times of educational reforms and attempts at closing achievement gaps, the positive influence of the school principal as capacity builder has been gathering much interest (Bredeson, 2003; Davies, 2009; DuFour, 1991; DuFour, DuFour Eaker, 2008; Leithwood, K., Seashore-Lewis, Anderson, S. Wahlstrom, K., 2004;Venables, 2011). But if Liethwood et al. (2004) argue that the principal's influence on student achievement comes second only to the teacher's influence, much of how such influence is exerted within school communities remains to be uncovered. The conceptual framework from the study is inspired by Timperley and Alton-Lee's (2008) iterative cycle of teacher professional development, where teacher professional development is followed by changing teaching practices which in turn, is informed by student outcomes. The present conceptual framework has adapted the model to include the school principal. The purpose of this study was to gain a better understanding of the principal's leadership role as it pertains to the role's reality as a school capacity builder, asking the question: How do school principals influence the professional development of teachers to support teachers' instructional practices for student learning? The study attempted to answer the following three sub questions: 1) How do principals perceive their role in influencing the professional development of their teachers? 2) What are the strategies employed by principals to support teachers' professional development? 3) What are the barriers to supporting teachers' professional learning and growth that school leaders face daily? Semi-structured interviews were conducted with fourteen principals, 10 elementary and four secondary. All principals were from publicly-funded school boards in the province of Ontario, Canada. Main impediments to building school capacity include the principals' continuous struggle between administrative and leadership duties, the lack of autonomy in the planning of professional development, the lack of time for follow ups and the centralized nature of boards' professional development plans. Implications for policy, practice and future research are discussed.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".