A focus on the <i>how</i> of meaningful physical education in primary schools
Bibliographic record
Abstract
In line with recent calls for clearer connections to be made between the why, what and how of physical education, there has been a renewed emphasis on the value of promoting meaningful experiences for young people. While attention had been paid to the why and what of meaningful physical education, less has been directed toward the how. Therefore, the purpose of this study was to examine ways that the features of meaningful experiences (the what) – including social interaction, fun, challenge, motor competence and personally relevant learning – provided guidance for one teacher’s planning and instructional decisions and the enactment of particular pedagogical strategies (the how) that promote meaningful physical education experiences in one primary teacher’s class. Data from a unit of striking-and-fielding games were collected and analysed. Pedagogical strategies that helped the teacher to promote each feature of meaningful experiences are outlined in conjunction with supporting teacher and student data. This study offers preliminary insight into how a teacher can promote meaningfulness in physical education by offering direction on particular pedagogical strategies that begin to form a coherent approach for physical education practice.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".