Application of the fuzzy clusterwise generalized structured component method to evaluate implementation of national education standard in Indonesia
Bibliographic record
Abstract
Results of school accreditation and national examinations are two indicators that are often used to describe the achievement of quality in education in Indonesia. ‘Accreditation’ reflects the fulfillment of 8 national education standards (NES), while the national exam (NE, or UN in Bahasa) for students describes academic performance. Eight NES and academic performance are latent variables. The relationship between the two variables and the validity of its indicators can be evaluated by several methods. Path analysis with latent variables can be obtained through general structured component analysis (GSCA) with the assumption of homogeneity of variance. Since the data are not homogeneous, this study aims to apply the fuzzy clusterwise generalized structured component analysis (FCGSCA) to evaluate the relationship between the NES and the UN, and the validity of the indicators. The results showed that there were two school clusters in Indonesia. The evaluation of the measurement model indicated that some indicators of the accreditation instrument were not valid, i.e., 6 indicators in cluster 1 and 15 indicators in cluster 2. The structural model evaluation of the two clusters indicated that standard of process to the UN was not significant. Based on the overall goodness of the fit model, the total diversities of all variables that could be explained were 61.60% in cluster 1 and 59.90% in cluster 2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".