PENGEMBANGAN MODEL PEMBELAJARAN TEMATIK SENI DAN BUDAYA MENGGUNAKAN VIDEO TEATERIKALISASI COWONGAN DI SEKOLAH DASAR
Bibliographic record
Abstract
This study aims to develop a thematic learning model of art and culture usingvideo theatericalization of “Cowongan” for students in elementary schools. Thisresearch is a research development that aims to improve the ability to verbalize“Kidung Kasengsaraan Mangsa Ketiga” with the Banyumas dialect. The researchdesign uses R and D Data collection techniques in research gather information, productdesign, product validation, product testing, design revision, trial use, product revision,mass production, and dissemination. These data sources are interviews, observations,taking questionnaires, and tests. The sampling technique uses cluster sampling as thepopulation of SDN 4 Sokanegara students. The results of research in verbalizing thegeguritan include laval, intonation, placement of pauses, and expressions. Theobservations of researchers obtained data that the average posttest results that theexperimental class reached 90% of the expected score, while the average control classreached 75.75% of the expected score. The results of the effectiveness test in theresearch prove that the class that uses the product is better than the one that does notuse the product so that the ability to speak up using the Banyumas dialect is moreeffective.
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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".