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Record W4401391639 · doi:10.3390/educsci14080850

Fostering Competence and Autonomy in High School Physical Education Classes: An Exploration of Intricate Relationships

2024· article· en· W4401391639 on OpenAlexaff
Matt Alexander Taylor, Kevin MacLeod

Bibliographic record

VenueEducation Sciences · 2024
Typearticle
Languageen
FieldPsychology
TopicMotivation and Self-Concept in Sports
Canadian institutionsRoyal Roads University
Fundersnot available
KeywordsAutonomyCompetence (human resources)PsychologyMathematics educationPedagogyPhysical educationSocial psychologyPolitical science

Abstract

fetched live from OpenAlex

This study addresses concerns surrounding the assessment of competence through various fitness tests in physical education, specifically assessments misaligning with the conceptualization of physical literacy. The study aimed to deductively analyze student assessment experiences with principles of self-determination theory, focusing on the role of competence in supporting autonomy. Particular instruments, such as the vertical jump and 30 m sprint tests, observed high levels of student preference and perceived significance. Interestingly, while the multistage fitness test was identified by students as a reliable indicator of physical fitness, it garnered limited student selection. While specific movement recommendations are not outlined, the findings underscore several noteworthy considerations. Notably, various factors influence student choices in student-selected fitness assessments, and purpose-driven fitness assessments can contribute to student motivation. The study’s insights provide valuable guidance for structuring physical education programs to foster engagement and autonomy among students.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.317

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
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 head0.157
GPT teacher head0.414
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations3
Published2024
Admission routes1
Has abstractyes

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