Assessment of Preschool Children's Physical Readiness: Advancing Early Madani Education in Selangor
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".