Curriculum Development Based on Local Cultural Wisdom at Al Jannah Early Childhood Education Unit Magelang Regency
Bibliographic record
Abstract
This study aims to develop a curriculum for the Al Jannah Early Childhood Education (PAUD) unit that can optimize the integration of local cultural wisdom. This study uses the Four-D model where this model can be seen as a model for instructional development, namely: 1) Define; 2) Design; 3) Development; 4) Dissemination. The feasibility of the local cultural wisdom-based curriculum development product at the Al Jannah PAUD unit has been tested through expert validation and teacher perception analysis. The validation results from material experts show an average feasibility percentage of 77.22% (category "Good"), with the curriculum objectives component getting the highest score (86%) and the content/material component getting the lowest score (70%). Meanwhile, validation from cultural experts shows a higher feasibility percentage of 85.88% (category "Very Good"), with the Local Language aspect getting a perfect score (100%), followed by Regional Stories and Legends and Celebrations of Local Holidays (93.33%). The results of the teacher perception and readiness questionnaire also showed a very positive response (86% - "Very Good" category), although there is a gap between positive perceptions and implementation readiness, especially in terms of the availability of resources and teaching materials (20% - "Poor" category). Overall, the developed curriculum has met the eligibility standards for implementation in the Al Jannah PAUD unit with several improvements in the content/materials and implementation support aspects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".