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Record W4401763231 · doi:10.4025/jphyseduc.v35i1.3506

The effect of game-based karate training on the learning of basic techniques and enjoyment of physical activity in children

2024· article· en· W4401763231 on OpenAlexaboutno aff
Amin Gholami, S Mousavi, Malihe Naeimikia, Pouya Sofizadeh

Bibliographic record

VenueJournal of Physical Education · 2024
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyPhysical educationMathematics educationTraining (meteorology)Learning effectGeography

Abstract

fetched live from OpenAlex

The purpose of this study was to investigate the effect of game-based karate training on learning basic karate skills and karate children's interest in physical activity. The statistical population of this research included all 6 to 8-year-old female karate players in Sowmeh Sara city, Iran and twenty subjects were randomly selected and divided into two experimental and control groups of 10. The experimental group practiced game-based karate training for eight weeks. The training program was designed based on the principles of the Fundamental stage of the Canadian model of long-term development of athletes (LTAD) for karate sport. Physical activity enjoyment scale in children was used to measure children's interest in physical activity and karate yellow belt test was used for measurement of learning basic karate tasks. ANCOVA test was used for data analysis using SPSS 24 software at a significance level of 0.05. The results showed that there was no significant difference in the learning of basic karate skills, but a significant effect was seen in the enjoyment of children in the experimental group for physical activity (sig<0.001). Therefore, game-based karate training can help children learn basic karate skills as much as the traditional method, and also increase their enjoyment in physical activity more effectively

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.759
Threshold uncertainty score0.469

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.043
GPT teacher head0.479
Teacher spread0.436 · 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 designOther design
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
Published2024
Admission routes1
Has abstractyes

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