Socioscientific Issue-Based Science Learning: How It Relates to Sustainable Education?
Bibliographic record
Abstract
Education for Sustainable Development can be developed by incorporating key sustainable development issues included in the Socioscientific issue (SSI). In science learning, SSI can help students contribute to decision-making, understand the nature of science, gain experience in discussing controversial issues, and increase their environmental awareness. The research surveyed the implementation of science teachers' SSI-based science learning (Biology / Physics / Chemistry). The research population was 39 high schools in Banyumas Regency, consisting of 14 public and 25 private high schools. The research subjects were science teachers (biology/physics/chemistry) and students in those high schools—sample collection using simple random sampling. Data collection instruments used include questionnaires, interview sheets, and observation sheets. Data collection methods include Surveys, interviews, and observation. The data analysis is carried out in three ways: data reduction, data presentation, and verification/conclusion. The data analysis technique was carried out using the percentage technique. The results showed that the profile of SSI-based science learning in high schools in the Banyumas district, both in public and private high schools, is in the excellent category with a percentage value of 84.5%, which means that high school teachers in the Banyumas district have integrated SSI well into classroom learning activities. Aspects of design elements in SSI-based learning have an average percentage of 86.7% (excellent), aspects of student experience have an average percentage of 83.1% (excellent), and aspects of teacher attributes have an average percentage of 83.6% (excellent).
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".