Meningkatkan Kemampuan Menyimak melalui Metode Bercerita pada Anak Usia Dini
Bibliographic record
Abstract
The study aims to improve the listening ability through the method of telling the story of the children of group A TK Pertiwi Rejowinangun Selatan Magelang City. Research is a classroom action research consisting of four stages: planning, implementation, observation and reflection. The research was conducted in TK Pertiwi Rejowinangun Selatan Kota Magelang. The subjects of this study are students of Group A of TK Pertiwi Rejowinangun Selatan Kota Magelang, amounting to 12 children. Variables used in the study include input variables (listening ability before action), process variables (storytelling method) and output variables (listening ability after action). Methods of data collection using observation method. Methods of data analysis using descriptive statistical analysis percentage, with success indicator> 75%. The conclusion of the research result proves that storytelling is effective to improve the listening ability in group A TK Pertiwi Rejowinangun Selatan Magelang City. The result of preliminary observation is known that average listening ability only reach 60,9%. After the learning activities using the storytelling method, the average achievement of listening ability of subjects increased to 86.1%, has exceeded the target set that is> 75%.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".