Learning about meaningful physical education in physical education teacher education: two case studies of sustained implementation
Bibliographic record
Abstract
How innovations are interpreted and implemented in teacher education programmes matters to how they are applied in school contexts. Implementation research can illuminate how innovations promote educational effectiveness [Century, J., & Cassata, A. (2016). Implementation research: Finding common ground on what, how, why, where, and who. Review of Research in Education, 40(1), 169–215]. Eight teacher educators explored how the Meaningful Physical Education (PE) pedagogical innovation was taken up and implemented across time in two physical education teacher education programmes. Data sources included two structured focus group interviews and sample course documentation and materials. Within-case analysis illustrates how attributes of the Meaningful PE innovation, organisational and contextual factors, and supports influenced implementation. The cross-case analysis explores how and why Meaningful PE was sustained. Notably, when Meaningful PE ideas were embedded within and across programme structures based on a shared vision, implementation was sustained. Moreover, Meaningful PE was vulnerable to organisational (e.g. personnel) and environmental changes that risked its inconsistent implementation. Grounding innovation elements in programme structures may help sustain a consistent implementation across time.
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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 | 0.031 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.012 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.008 | 0.011 |
| 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".