Contextual coaching: levering and leading school improvement through collaborative professionalism
Bibliographic record
Abstract
Purpose The research examines how contextual coaching (Gorrell and Hoover, 2009; Valentine, 2019) can act as a lever to build collaborative professionalism (Hargreaves and O'Connor, 2018) and lead to school improvement. Design/methodology/approach The multi-case study (Stake, 2013) draws on two bespoke examples of contextual coaching in education and uses the ten tenets of collaborative professionalism as a conceptual framework for its abductive analysis. Data from both cases were collected through interviews, focus groups and documentation. Findings The findings demonstrate that effective contextual coaching leads to conditions underpinning school improvement. Specifically, there are patterns of alignment with the ten tenets of collaborative professionalism. Whereas contextual coaching is found on four of these tenets (mutual dialogue, joint work, collective responsibility and collaborative inquiry), in more mature coaching programmes, three others (collective autonomy, initiative and efficacy) emerge. There is also evidence that opportunities exist for contextual coaching to be further aligned with the remaining three tenets. The study offers insight into how school improvement can be realized by the development of staff capacity for teacher leadership through contextual coaching. Research limitations/implications The impact of coaching in education is enhanced by recognizing the importance of context and the value of iterative design and co-construction. Practical implications The principles of contextual coaching are generalizable, but models must be developed to be bespoke and to align with each setting. Collaborative professionalism offers a useful framework to better design and implement contextual coaching programmes. Originality/value The research introduces contextual coaching in education, and how coaching can enhance collaborative professionalism in schools.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.019 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".