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Record W2166613807 · doi:10.5539/ass.v10n5p123

The Influence of Self Regulated and Traditional Learning Model on the Development of Students’ Cognitive Process and Sport Enjoyment in Basketball Learning Process

2014· article· en· W2166613807 on OpenAlexvenueno aff
Dian Budiana

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsnot available
Fundersnot available
KeywordsBasketballPsychologyCognitionPhysical educationProcess (computing)Mathematics educationSelf-regulated learningApplied psychologyComputer science

Abstract

fetched live from OpenAlex

One of the critical issues in physical education is creating a fun physical education which can develop students’ cognition without ignoring the mastery of basic technique through the implementation of a learning model. To overcome the critical issue, an experiment using self regulation and traditional learning models have been applied to 60 junior high school students using randomized pretest-posttest control group design. The instruments used are questionnaire of cognition process development and sport enjoyment of which the validity and reliability have been tested. Based on the data analysis, results showed that there is an influence between the learning model and the development of cognition and sport enjoyment in basketball game learning; however, self regulation learning model gives more significant influence compared to that of traditional learning model. This model is recommended to be used by physical education teachers in junior high school. This model can be an alternative of innovation to the learning process of physical education because it is proven to significantly influence students’ cognitive development and sport enjoyment in basketball learning process.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.378
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
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 head0.053
GPT teacher head0.441
Teacher spread0.387 · 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 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

Citations4
Published2014
Admission routes1
Has abstractyes

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