Implementing Scientific Approach to Teach English at Senior High School in Indonesia
Bibliographic record
Abstract
Scientific approach is a teaching strategy using scientific steps in teaching subject matter at senior high school in Indonesia. Scientific approach has the characteristics of “doing science” that allows teachers to improve the process of learning by breaking the process down into steps which contain detailed instruction for conducting student learning. Although the scientific approach offers significant breakthrough in improving the quality of teaching English as a foreign language (TEFL) at Senior High School in Indonesia, there were still some obstacles faced by English teachers. This study aimed at investigating the implementation of scientific approach to teach English at Senior High School in Indonesia and problems of teaching and learning in implementing scientific approach. The data were collected through observation of teaching learning process and interview with the teachers and the students in two senior high schools in Padang, Indonesia. The findings showed that, among the five steps of scientific approach, the teachers were not able to implement the observing and questioning steps optimally yet. Meanwhile, in experimenting and associating the teachers have applied them well, and in communicating the teachers have applied them optimally.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".