Administrator Perspectives on Teacher Leadership for Teaching Students with Extensive Support Needs Across Settings
Bibliographic record
Abstract
While often understated, the role of teachers of students with extensive support needs (ESN) requires many activities associated with the concept of teacher leadership. Administrators play a key role in supporting teachers as they perform these essential leadership-oriented activities. In this study, researchers surveyed the engagement and perspectives of administrators in general and special education settings on six teacher leader competencies (TLCs) related to teaching students with ESN. Overall, special and general education administrators reported high confidence in their own ability to supervise teachers in the TLCs as well as in their teachers’ ability to implement four of the six TLCs. While no significant differences were found between the two administrator groups, some differences were noted between the type of professional development received and the levels of expectation for the following TLCs: implementing inclusive practices, supervising paraprofessionals, and adapting and developing individualized curricula. Given that administrator support has an important impact on teacher job satisfaction and retention, this study provides discussion regarding the findings and potential implications for the field.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".