Developing a Model of English Digital Poster Book for Teaching English in Indonesia’s Early Childhood Education
Bibliographic record
Abstract
Teaching English to Young Learners (TEYL) means introducing English as a foreign language in learning to children. Children should learn a foreign language earlier to expand the boundaries of the world and encourage them to face dynamic international development. Nowadays, children are immersed as native digital users more so than generations past. Therefore, this research is a research and development (R&D) that aims to design, develop and validate an English digital poster book Model teaching English in Indonesia’s early childhood education using the ADDIE Models. This model consists of five steps: analyze, design, develop, implement, and evaluate. The product of this research was developed and then evaluated by four content experts. The result showed that all content experts considered that the final version of an English digital poster book for teaching English in Indonesia’s early childhood education was practical and valid. Based on the observation, most children are excited to use this product in learning English. Teachers also claimed that this product was so interesting, easy to understand, appropriate to the level of difficulty for children in learning English, easy to operate, and can guide them to in teaching English at kindergarten level.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".