‘It’s how PE should be!’: Classroom teachers’ experiences of implementing Meaningful Physical Education
Bibliographic record
Abstract
Meaningful Physical Education (PE) is a pedagogical approach to PE instruction designed with the aim of helping teachers explicitly prioritise meaningful experiences for students. The purpose of the current study was to conduct a small-scale implementation of a preliminary version of Meaningful PE with a sample of five primary classroom teachers in Ireland to receive their feedback on the approach and their experiences of implementing it in their classrooms. Qualitative data were collected across an eight-week implementation period. An actor-oriented analysis was used to focus specifically on teachers’ decisions concerning both what and how to implement the approach, as well as the reasons why they implemented Meaningful PE the way they did. Results show teachers were generally supportive of Meaningful PE as they attempted to implement several components of the approach in their classrooms. Teachers’ implementation was highly related to their positive interpretations of the approach, in relation to both their perceptions of beneficial student outcomes and in drawing connections between the approach and prior experiences of and beliefs about teaching. This study adds further support to prior small-scale studies where implementation of Meaningful PE has been assessed and provides insight into how the approach might be introduced to and implemented more broadly by teachers in the future.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".