Towards developing and validating Quality Physical Education in schools—The Asian physical education professionals' voice
Bibliographic record
Abstract
Physical education professionals aim to develop quality programmes for physical education. This study aimed to develop and validate a scale using professionals' perceptions of Quality Physical Education QPE in Asia using twenty-four items regarding QPE quality issues. The items covered status and roles, development of educational elements and supportive features in physical education. A sample of N = 799 sport and physical education professionals from eleven Asian cities participated in this questionnaire survey. Twenty-four items relating to QPE were examined via exploratory factor analysis (EFA) using maximum likelihood extraction and direct oblimin rotation methods. Nevertheless, only 20 items were extracted following the EFA examination. Items 1, 9, 14 and 18 were excluded because of low factor loadings. The remaining items were clustered into four subscales: Development and Supportive Elements for Quality Physical Education in Schools (DSFQPE; α = .918), Core Values of Quality Physical Education (CVPE; α = .908), Curriculum Arrangement of Physical Activities (CAPA; α = .884) and Provision and Norms in Physical Education (PNPE; α = .865). The Cronbach's alpha coefficient (α = .875) indicated excellent internal consistency for the overall measure. Furthermore, the 4 retained factors from the EFA were assessed via robust confirmatory factor analysis (CFA). The 4-factor model demonstrated a good fit with the data (CMIN/DF = 3.450, CFI = .928, TLI = .916, PCFI = .801, RMSEA = .078). The study identified a 4-factor structure with internal consistency and acceptable interfactor correlations. The structure seemed to be applicable, including the twenty items identified as useful and necessary tools for the framework of analysis in the investigation of diverse settings for the study of quality physical education.
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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 | 0.067 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".