The Development of Innovative Chemistry Learning Material for Bilingual Senior High School Students in Indonesia
Bibliographic record
Abstract
The development of innovative chemistry learning material for bilingual Senior High School (SHS) students in Indonesia is explained. The study is aimed to obtain an innovative chemistry learning material based on national curriculum in Indonesia to be used as a learning media in the teaching and learning activities. The learning material is developed by enhancing the chemistry topics to meet the requirement of a national curriculum followed by integration of laboratory experiments, learning media, and contextual application of the relevant chemistry topics. The material is then designed in printed and electronic bases. The performance of developed chemistry material is standardized to meet good quality learning material for class purposes. The results showed that the performance of developed chemistry materials is categorized as very good. The developed learning material is found effective to be used in teaching and learning process, and be able to motivate the students to learn chemistry. The facilities provided in the material are adequate to guide the student to study chemistry independently that make learning activities moving from teacher centre learning become students centre learning. Students achievements in experimental class (M = 83.0) is found higher than that with control class (M = 73.5), where both are significantly different. There is a positive correlation between the student’s motivations with the student’s achievement in chemistry subject, where the correlation in experimental class (R² = 0.711) is better than in control class (R² = 0.467).
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".