English for Young Learning Method through Games and Songs for Elementary School
Bibliographic record
Abstract
This study aims to determine the role of music and games in elementary school children's learning. The method used in this research is a type of qualitative research. The data collection process was carried out using the Focus Group Discussion (FGD) model in natural settings (natural conditions), primary data sources, and more data collection techniques on observation, in-depth interviews (in-depth interviews), and documentation. The chosen research location is an elementary school in Tulungagung Regency. Teaching English to young learners for teachers is fun. Teachers are required to learn in an interesting and not monotonous manner. Young learners also have a good memory in responding to something. Young learners are active students, so playing is one of the things they enjoy doing. So, the teacher must have innovations to create a learning atmosphere that is not monotonous. One of the things that can be done is to do learning by applying games in class. Young learners in learning English need various interesting methods to be applied in class. One technique that can be used for young learners in learning English is song and games. The benefit for young learners learning English is that they can speak English in the future. The aim of young learners learning English is to master as many vocabulary words as possible. Therefore, they will not experience any difficulties using English in the future. That way, English can be used for their skills in the future.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".