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Record W4411152532 · doi:10.5539/jel.v14n6p103

“Why Test Me If You Don’t Teach Me?” Rethinking Physical Education Through a Lifelong Learning Lens

2025· article· en· W4411152532 on OpenAlexvenueno aff
Monticha Uraipong, Chatchawoot Pojsompong, Dech-siri Nopas

Bibliographic record

VenueJournal of Education and Learning · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsnot available
Fundersnot available
KeywordsLifelong learningPsychologyPedagogyMathematics educationTest (biology)Physical educationLens (geology)Optics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.685
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.058
GPT teacher head0.461
Teacher spread0.404 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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