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Record W4402814141 · doi:10.6007/ijarbss/v14-i9/22749

Case Study on Fine Motor Skills Development in Early Childhood Education

2024· article· en· W4402814141 on OpenAlexaff
Nik Evina Nik Roseli, Siti Farhana Binti Md. Yasin, Mohd Azim bin Sharim, Kalaivani Vijayaragavan

Bibliographic record

VenueInternational Journal of Academic Research in Business and Social Sciences · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Education
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsMotor skillPsychologyDevelopmental psychologyEarly childhood education

Abstract

fetched live from OpenAlex

This study explores the impact of educational strategies on the development of fine motor skills among preschoolers in Klang, Malaysia, focusing on the effectiveness of specific activities and pedagogical approaches. Using a qualitative research design, the study involves semi-structured interviews and classroom observations to gather insights into teacher-child interactions. The sample includes five- and six-year-old children and their teachers, selected through purposive sampling. Thematic analysis reveals that activities requiring precision and control, along with peer interactions and collaborative tasks, significantly enhance fine motor skills. The findings emphasize the importance of developmentally appropriate, engaging activities, individualized instruction, and creative learning modalities in supporting children's cognitive and physical development, ultimately preparing them for future academic success.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.117
GPT teacher head0.486
Teacher spread0.369 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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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