Enhancing Students’ Science Literacy through Megedong-Gedongan: A Balinese Local Culture-based Flipbook
Bibliographic record
Abstract
This study investigates the effectiveness of the Balinese megedong-gedongan local culture-based flipbook in enhancing science literacy among students. This research aims to increase scientific literacy through flipbooks based on Balinese culture, especially megedong-gedongan. Mastery of scientific literacy opens the door to various job opportunities in science, technology, and innovation. In the digital and knowledge-based era, the demand for human resources with a strong understanding of science and technology continues to increase. Employing a classroom action research methodology, the study involved 47 Biology and Marine Affairs students at the Ganesha University of Education, Indonesia. The study used a valid and reliable scientific literacy test as an instrument. The results reveal that the Bali megedong-gedongan flipbook effectively improved science literacy. This is evidenced by a significant increase in student science test scores after using the flipbook. The data analysis results show that using megedong-gedongan Balinese culture-based flipbooks can significantly increase student scientific literacy. In addition, students more easily relate learning material to their daily lives, which impacts increasing scientific literacy. This research suggests that integrating local culture into science teaching materials can effectively increase scientific literacy among students.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".