TRILINGUAL LEARNING MODEL THROUGH TRADITIONAL GAMES: AN OVERVIEW FROM AN INDONESIAN KINDERGARTEN
Bibliographic record
Abstract
This study proposes the application of trilingual learning model that employs Sundanese traditional playing as the learning strategy. This article is a part of a bigger ongoing research project investigating the establishment of trilingual learning model in kindergarten. It focuses on the development of lesson plans for the application of the teaching and learning using three languages, namely; Sundanese, Indonesian and English. Qualitative method is employed as the research method. The subjects of the study are three teachers who are fresh graduates from Study Program of Early Childhood Education Teacher (henceforth, PGPAUD) Universitas Pendidikan Indonesia (UPI) Kampus Cibiru. Those teachers are asked to apply this learning model to Kindergarten children in one of Indonesian kindergarten in Bandung. The result of the study suggests that there are three stages in trilingual lesson plan development. The stages are: 1) determining indicators of developmental aspects based on 2013 Indonesian Early Childhood Education (ECE) curriculum, 2) modifying Sundanese traditional playing to be applicable for kindergarten children and designing the applicability of those playing to be implemented in trilingual, 3) designing trilingual learning media and 4) designing the assessment process to observe children’s developmental 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".