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Record W3025974117 · doi:10.5539/jel.v9n3p73

Determining the Motor Ability Levels of the Preschool Children

2020· article· en· W3025974117 on OpenAlexvenueno aff
Zehra Gözel Tepe

Bibliographic record

VenueJournal of Education and Learning · 2020
Typearticle
Languageen
FieldPsychology
TopicChildren's Physical and Motor Development
Canadian institutionsnot available
Fundersnot available
KeywordsJumpingMulti-stage fitness testPsychologyMotor skillFlexibility (engineering)JumpDevelopmental psychologyTest (biology)Descriptive statisticsPhysical fitnessPhysical therapyStatisticsMedicineMathematics

Abstract

fetched live from OpenAlex

In this study, it was aimed to investigate the motor ability levels of the preschool children. The sampling of the study consisted of 46 children (22 girls, 24 boys) between the ages of 5-6. Kindergarten Mobile Test (KiMo) was used in determining the motor ability levels of the children. The test consisted of 5 subtests. These were; the shuttle run, standing long jump, one leg stand, flexibility and lateral jumping. Descriptive statistics were used to identify the average, frequency and percentage distributions regarding the motor abilities of the children. The children achieved average and below-average scores at all age groups in the shuttle run, standing long jump, one leg stand, flexibility and lateral jumping and they were incompetent in coordination, endurance and speed as basic motor abilities. As a result, it was determined that the motor abilities of the preschool children were low as of the early years. Performing activities that involve motor abilities inside and outside the school for the preschool children, who spend most of the day in narrow locations with limited movement areas, would support their healthy development in physical, mental and social terms.

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 score0.192
Threshold uncertainty score0.233

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 head0.022
GPT teacher head0.298
Teacher spread0.275 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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