MELALUI SUPERVISI AKADEMIK YANG BERKELANJUTAN DAPAT MENINGKATAN KOMPETENSI GURU DALAM MENYUSUN SILABUS DAN RPP DI SMK NEGERI 8 BUNGO
Bibliographic record
Abstract
This research is based on the importance of teachers planning, implementing and evaluating learning. Syllabus and lesson plans are the minimum preparation for a teacher when they want to teach. With these problems, researchers conducted research to see to what extent the academic supervision of school principals could improve teacher competence in the preparation of syllabus and lesson plans. The type of research used in this research is School Action Research, which consists of planning, implementation, observation and reflection. The subjects of this study were all teachers of SMK Negeri 8 Bungo. Data collection techniques used in this study include supervision, observation, semi-structured interviews and documentation. The results of the action show that: 1). With the increase in the number of teachers who compose the syllabus from 38.46% to 83% after academic supervision, 2) the number of quality lesson plans increased from 38.46% to 89%. So, by conducting academic supervision, it can improve teacher competence in compiling the Syllabus and RPP of SMK Negeri 8 Bungo.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".