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Record W4407280518 · doi:10.36706/altius.v13i1.34

The physical literacy level of elementary school students was examined from the motivation and self-confidence domains

2024· article· en· W4407280518 on OpenAlexaboutno aff
Johan Irmansyah, Eka Safitri Diningsih

Bibliographic record

VenueAltius Jurnal Ilmu Olahraga dan Kesehatan · 2024
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsSelf-confidenceMathematics educationPsychologyPhysical educationSelf-efficacyLiteracyDevelopmental psychologyPedagogySocial psychology

Abstract

fetched live from OpenAlex

Physical education learning in elementary schools has a significant role in developing students' physical literacy. However, physical literacy is not only related to physical abilities, but is also influenced by psychological factors, such as students' motivation and self-confidence in the learning context. This study aims to analyze the domains of motivation and self-confidence using the Canadian Assessment of Physical Literacy (CAPL-2) Questionnaire in elementary school students. The design used in this research is a cross-sectional study design, which aims to collect data at one time (point time approach). The research results obtained that the percentage of motivation and self-confidence scores for elementary school students is as follows: beginning 0%; progressing 16.67%; achieving 33.33%; excelling 50%. The data shows that elementary school students have excellent levels of motivation and self-confidence (excelling category) and have exceeded the recommended physical literacy levels associated with substantial health benefits. In the context of physical education learning in primary schools, strengthening students' motivation and building self-confidence will not only improve their physical skills, but will also help them develop positive attitudes towards physical activity and overall health.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.437
Threshold uncertainty score0.681

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.030
GPT teacher head0.323
Teacher spread0.293 · 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

Explore more

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