“Why Test Me If You Don’t Teach Me?” Rethinking Physical Education Through a Lifelong Learning Lens
Bibliographic record
Abstract
This study explores secondary school students’ experiences with physical education (PE) in Thailand, with a particular focus on the impact of standardized fitness assessments on motivation, self-perception, and long-term engagement in physical activity. Using a qualitative approach, data were collected through interviews, focus groups, and classroom observations with 13 students from three schools. The findings reveal contrasting student experiences—some found PE enjoyable and empowering, while others described it as stressful and exclusionary. Standardized assessments were often perceived as demotivating, especially by students who struggled to meet benchmarks, leading to negative self-perceptions and disengagement. Gender norms and peer influence further shaped participation, with male students reporting pressure to perform and female students frequently experiencing marginalization in sports. Students also expressed that PE lacked relevance to real-life physical activity and failed to promote lifelong fitness habits. The study advocates for a shift toward a lifelong learning-oriented PE model that prioritizes personal progress, movement diversity, inclusive pedagogy, and health literacy. These findings highlight the need to reframe PE as a supportive, student-centered space that empowers learners to develop lasting, positive relationships with physical activity.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.006 |
| 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".