Is distributed leadership an effective approach for mobilising professional capital across professional learning networks? Exploring a case from England
Bibliographic record
Abstract
Purpose There is currently a focus on using networks to drive school and school system improvement. To achieve such benefits, however, requires school leaders actively support the mobilisation of networked-driven innovations. One promising yet under-researched approach to mobilisation is enabling distributed leadership to flourish. To provide further insight in this area, this paper explores how the leaders involved in one professional learning network (the Hampshire Research Learning Network) employed a distributed approach to mobilise networked learning activity in order to build professional capital. Design/methodology/approach A mixed methods approach was used to develop a case study of the Hampshire RLN . Fieldwork commenced with in-depth semi-structured interviews with all school leaders of schools participating in the network and other key participating teachers (12 interviews in total). A bespoke social network survey was then administered to schools (41 responses). The purpose of the survey was to explore types of RLN-related interaction undertaken by teachers and how teachers were using the innovations emerging from the RLN within their practice. Findings Data indicate that models of distributed leadership that actively involves staff in decisions about what innovations to adopt and how to adopt them are more successful in ensuring teachers across networks: (1) engage with innovations; (2) explore how new practices can be used to improve teaching and learning and (3) continue to use/refine practices in an ongoing way. Originality/value Correspondingly we argue these findings point to a promising approach to system improvement and add valuable insight to a relatively understudied area.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".