ANALYSIS OF COGNITIVE ASPECTS IN EARLY CHILDHOOD LEARNING: A STUDY ON THE IMPLEMENTATION OF THE MERDEKA CURRICULUM
Bibliographic record
Abstract
The aim of this writing is to analyze the cognitive aspects of early childhood learning using the Merdeka curriculum. The research method employed is the qualitative descriptive method. Data collection techniques were conducted through observation, interviews, documentation, and data analysis techniques. Descriptive data analysis was carried out to explain or present information contained in the data so that it could be better understood. The research results indicate that the application of cognitive theory in early childhood learning at Integrated Early Childhood Education Citra Bakti implements child development in accordance with the preparation of teaching aids where there are indicators related to cognitive aspects. In learning achievements, there are three stimulation elements, each containing six aspects. These three stimulation elements elaborate on the aspects of religious and moral values development, physical motor skills, cognitive, socio-emotional, language, Pancasila values, and other areas to optimize children's growth and development according to educational needs. In the Merdeka curriculum, learning achievements are outlined as learning objectives. In these learning achievements, the three achievements consist of: (1) achievement of religious and moral values learning, (2) achievement of self-identity learning, and (3) achievement of literacy basics, mathematics, science, technology, engineering, and arts learning. Article visualizations:
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".