English Teachers' Perception on the Implementation of 2013 Curriculum in Kawunganten, Cilacap
Bibliographic record
Abstract
Curriculum is essential for education in a country because it will portray the philosophy of life and maintain its identity. The primary purpose of a curriculum is to attain the educational goals of a country. In Indonesia, the national education curriculum has changed several times, and the latest curriculum is 2013 Curriculum. It applies a scientific approach to the learning process, consisting of five steps of thinking to gain knowledge: observing, questioning, experimenting, associating, and networking. Moreover, it is designed to anticipate the modern learning of the 21st century. Therefore, the English teachers face considerable challenges in teaching their students, and knowing their perception in implementing this curriculum is important to be discussed. The respondents of this study were 14 English teachers who were selected using a purposive sampling technique. The researcher used a close-ended questionnaire as the instrument to collect the data. Further, the researcher analyzed the data statistically and descriptively. Considering the result of the research, the junior high school English teachers in Kawunganten district, Cilacap regency, have positive ideas about some issues related to the implementation of the 2013 Curriculum, such as perception and implementation, teaching administration, learning sources, learning implementation, scientific approach, and assessment process.
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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 | 0.002 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".