Penerapan Model Supervisi SUKSES-ME untuk Membangun Penguatan Pendidikan Karakter di Sekolah
Bibliographic record
Abstract
Hasil deskripsi pengembangan Penguatan Pendidikan Karakter (PPK) di sekolah binaan sebelum implementasi model Supervisi Klinis-Kolaborasi, Penilaian Sendiri-Sejawat, Monitoring, dan Evaluasi (SUKSES-ME) menunjukkan bahwa, pengembangan PPK berbasis kelas pada Silabus dan RPP, PPK berbasis budaya pada kegiatan non kurikuler; membersihkan lingkungan sekolah, upacara bendera, menyanyikan lagu nasional/daerah, membaca buku bersama baru, dan bakti sosial serta pengembangan PPK pada proses pembelajaran masih belum optimum. Penelitian ini bertujuan untuk menerapkan model supervisi kreatif yaitu model SUKSES-ME untuk meningkatkan PPK di Sekolah Menengah Atas (SMA). Metode penelitian yang digunakan adalah penelitian deskriptif menggunakan uji t, untuk melihat PPK sebelum dan sesudah implementasi model SUKSES-ME di SMAS Nugraha Bandung. Hasil penelitian menunjukkan bahwa penerapan model SUKSES-ME dapat meningkatkan PPK di SMAS Nugraha Bandung, baik pada struktur kurikulum (silabus dan RPP), pengembangan PPK berbasis budaya sekolah, dan pengembangan PPK pada proses pembelajaran. Peningkatan tersebut signifikan dengan nilai p < 0.05. Kesimpulan dari penelitian, supaya penerapan model SUKSES-ME ini optimal, maka harus dilakukan secara berkelanjutan dan berkesinambungan mulai siswa masuk sampai lulus sekolah
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
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".