DEVELOPMENT OF MULTIFUNCTIONAL CUBOID APE TO STIMULATE MOTOR ACTIVITIES IN CHILDREN AGED 4-5 YEARS IN PAUD ISTIQOMATUDDIN NURUL MU'ARIF PIDIE
Bibliographic record
Abstract
Based on the author's initial observations at PAUD Istiqamatuddin Nurul Muarif regarding using APE during learning, the PAUD has used several APEs in learning. However, the APE used was not adequate to train fine motor development and did not vary to attract children's interest. The purpose of the study was to develop and see the feasibility of APE Multifunction Beams in developing fine motor skills for children aged 4-5 years. The type of research used is Research and Development (R&D) using the ADDIE model using Dick and Carey Theory which consists of five stages, namely Analysis, Design, Development, Implementation, and Evaluation. With instrument validation sheet material experts and media experts as well as children's observation sheets. Based on the assessment of 2 APE validation and 2 material experts, for material expert validation the total scores from V1 and V2 are 69 and 67, so the maximum total score is 80, so that the percentage results are 80% and 86% which fall into the very feasible category. While the APE validation scores V1 and V2 are 69 and 67, the number of frequencies is 16, so that the percentage results are 86.25% and 83.75% which are in the very feasible category. Then for the results of the observation sheet the child's fine motor skills got a score from the validator of 308, with the number of questions as many as 5 multiplied by 15 children, then the ideal total score obtained was 375 so as to obtain a percentage result of 82.13% which was included in the very feasible category. Based on these results, it can be concluded that the multifunctional APE beam can develop fine motor skills for children aged 4-5 years and is very suitable for use in the learning process.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".