Peningkatan Mutu Pembelajaran Melalui Supervisi Akademik Berdasarkan Mutu Standar Proses Pembelajaran
Bibliographic record
Abstract
Pendidikan bermutu menjadi indikator keberhasilan dalam peningkatan daya saing sumber daya manusia.Penelitian ini bertujuan untuk menganalisis kinerja pencapaian standar proses dan merekomendasikan kebijakan dalam peningkatan mutu pendidikan SD, SMP dan SMA . Penelitian dilakukan berdasarkan pendekatan kombinasi (Mixed Method) dengan analisis konten laporan pemetaan mutu dan kebijakan pendidikan menengah. Teknik pengumpulan data dilakukan melalui studi dokumentasi pemetaan mutu pendidikan dari Lembaga Penjaminan Mutu Pendidikan dan Rencana Strategis Pendidikan. Kegagalan mencapai standar ini diakibatkan rendahnya para guru dalam menyusun perencanaan dan melaksanakan proses pembelajaran. Supervisi pendidikan, pelatihan, dan pendampingan guru menjadi program yang perlu dilakukan untuk meningkatkan mutu standar proses pendidikan.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
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; both teacher heads agree on what is shown here.
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".