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Record W2555002486 · doi:10.5539/jel.v6n1p122

The Investigation of the Relation between Physical Activity and Academic Success

2016· article· en· W2555002486 on OpenAlexvenueno aff
Rüçhan İri, Serkan İBİŞ, Zait Burak AKTUĞ

Bibliographic record

VenueJournal of Education and Learning · 2016
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyAcademic achievementTest (biology)Significant differenceVertical jumpFlexibility (engineering)Academic yearMathematics educationJumpStatisticsMathematics

Abstract

fetched live from OpenAlex

The purpose of the study is to investigate the interaction among Physical Activity Levels (PAL), academic successes, perceived academic competency and Motor Skills (MS) of male and female students at the age of 14-17 in terms of gender variable. The PALs, perceived academic competency and academic successes were determined through International Physical Activity Questionnaire (IPAQ), Academic Competency Scale and General Academic Averages respectively. MS were tested by sit-and-reach flexibility, vertical jump, hand grip strenght and back and leg strength tests. After the data were entered into the SPSS 16,0 program, paired t-test was done in order to determine the difference between genders. Also, the interaction among PAL, academic success, academic competency and MS of male and female students were analysed through Pearson correlation analysis. As a result, it was found out that parameters related to the PAL and strength of male students are higher than those of female ones while female students’ academic success levels are better than those of male students. In addition, while no significant relation between academic success and PAL was found, a positive relation was determined the academic success and perceived academic competency of both genders.

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 score0.419
Threshold uncertainty score0.125

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 science0.0000.000
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 head0.028
GPT teacher head0.334
Teacher spread0.307 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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