How School Leaders Might Promote Higher Levels of Collective Teacher Efficacy at the Level of School and Team
Bibliographic record
Abstract
Leaders search for effective leadership practices to ensure success. A quantitative study was conducted to determine what behaviors a leader could use to improve collective teacher efficacy at the level of the entire faculty and at the level of grade level teams. This article focuses on using the inverse relationship between transformational leadership and collective teacher efficacy to strengthen efficacy of teachers of English Language Learners. The Collective Efficacy Scale (Goddard, 2001) was modified to measure the perceptions of teachers at both levels; entire faculty’s collective efficacy and the collective efficacy of their team. Thus, this article also provides leaders with important information regarding teaming within schools. The significant difference found between collective teacher efficacy at the level of school and team, provides important information for leaders to consider as they support professional learning teams. Success for all would be promoted as leaders increase efficacy within teams by employing the concepts of developing leadership teams and purposeful learning communities (Hill & Lundquist, 2008).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".