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Record W4407182203 · doi:10.6007/ijarped/v14-i1/24532

Assessment of Preschool Children's Physical Readiness: Advancing Early Madani Education in Selangor

2025· article· en· W4407182203 on OpenAlexaff
Maaruf Rabu, Mohamed Ayob Sukani, Muhammad Khairul Annuar Abd Ajis

Bibliographic record

VenueInternational Journal of Academic Research in Progressive Education and Development · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSports and Physical Education Research
Canadian institutionsEducation and Early Childhood Development
Fundersnot available
KeywordsPsychology

Abstract

fetched live from OpenAlex

This study aims to develop and sustain the Preschool Physical Readiness Assessment Instrument for Selangor (IPKFPs) to evaluate the physical readiness level of preschool students in Selangor, supporting the sustainability of early MADANI education. The study design adopts a quantitative approach, with data collection conducted using the IPKFPs and statistical analysis performed through descriptive and inferential methods using SPSS software. The findings indicate that the physical readiness level of students is at a very high level across most categories, with excellent motor skills (hands and fingers), with the highest mean values observed in activities such as kneading (4.91) and pinching (4.87). In the gross motor category (legs), activities such as running (4.75) and walking (4.73) recorded very high levels, while weaknesses were identified in skipping activities, with a mean value of 2.38. Overall, the level of physical development achieved an overall mean value of 4.44, reflecting a very high level of achievement. The IPKFPs have proven to be a holistic and effective assessment instrument, providing teachers with guidance to identify students' strengths and weaknesses and to plan more specific interventions. This study emphasizes the need for widespread implementation of the IPKFPs to enhance the quality of preschool education, which is in line with the aspirations of early MADANI education.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.137
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.051
GPT teacher head0.582
Teacher spread0.532 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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