Collaborative conditions for teacher professional growth: the role of network intentionality and leading curriculum learning efficacy
Bibliographic record
Abstract
Purpose Driven by the need to deepen understanding of the mechanisms driving teacher collaboration for enhancing teacher learning and practices, this study aims to investigate the influence of collaborative organizational conditions, network intentionality and efficacy for leading curriculum learning on teachers’ professional growth within the context of New Zealand’s Communities of Learning-Kahui Ako (CoL) policy. Design/methodology/approach The study employs survey design collecting perceptual data from teachers within two CoLs comprising 12 schools in New Zealand. Structural equation modeling is used to analyze the relationships between collaborative organizational conditions, teachers’ network intentionality, leadership efficacy and teacher professional growth. Findings The findings reveal that collaborative organizational conditions significantly impact teachers’ professional growth such as their new learning and enhanced practices. Furthermore, teachers’ network intentionality and efficacy for leading curriculum learning serve as mediators, amplifying the effects of collaborative organizational conditions on teacher professional growth. Specifically, teachers who are more confident in their leadership abilities and intentionally build professional relationships are better at using collaborative opportunities to address teaching challenges and bring innovation to their schools. Originality/value This study contributes to the existing literature by examining the interplay between organizational conditions, internal motivational drive for collaboration, and teacher professional growth within the context of CoL policy in New Zealand. It sheds light on the mechanisms driving teacher professional growth and offers insights for enhancing teacher collaboration and professional learning experiences within CoL networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".