Evaluation of the Effectiveness of the Civic Education Curriculum in Indonesia: A Case Study of the KTSP Curriculum, the 2013 Curriculum, and the Independent Curriculum
Bibliographic record
Abstract
This study evaluates the effectiveness of the civic education curriculum in Indonesia, including the Kurikulum Tingkat Satuan Pendidikan (KTSP), the 2013 Curriculum (K13), and the Independent Curriculum. This study uses the concurrent triangulation method with quantitative and qualitative data analysis. Data were collected through semi-structured interviews and questionnaires from citizenship teachers and principals in 85 schools in Tangerang City. The results of the study show that each curriculum has advantages and disadvantages. KTSP is considered flexible and adjusted to school conditions, but the learning method tends to be monotonous. K13 emphasizes character development and authentic assessment more but experiences obstacles in implementation with long lesson hours. The Independent Curriculum provides freedom of exploration and encourages student independence and creativity but requires more excellent resources and the potential for free time. Glickman's quadrant analysis and Bradley's evaluation model were used to measure curriculum effectiveness. The results show that the Independent Curriculum is considered the most effective in character development and active learning, followed by K13, which stands out in authentic assessment and social skills. This study suggests that the implementation of the curriculum be adjusted to local needs and student characteristics to increase the effectiveness of civic education in Indonesia.
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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 | 0.012 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".