Supervisi Klinis Untuk Meningkatkan Kemampuan Literasi Digital Guru SMK Negeri Maniis Purwakarta
Bibliographic record
Abstract
Kegiatan pembelajaran di sekolah pada saat ini harus mulai menyesuaikan dengan tuntutan era 4.0. Penyesuaian tersebut diantaranya mengimplementasikan kemampuan literasi digital. Tujuannya membuat pembelajaran menjadi lebih menarik minat belajar siswa. Penelitian ini bertujuan untuk: (1) meningkatkan kemampuan guru dalam membuat soal menggunakan aplikasi kahoot dan (2) meningkatkan kemampuan siswa dalam mengerjakan soal yang dibuat guru dengan mengakses soal tersebut melalui browser web menggunakan smartphonenya (android). Metode penelitian yang digunakan adalah penelitian tindakan sekolah, yaitu melaksanakan pembinaan bagi sekelompok guru di suatu sekolah, melalui beberapa siklus, mengunakan sistem spiral refleksi model Kemmis dan Mc Taggart yang dimodifikasi. Strategi/Metode/Teknik Pembinaan yang digunakan pada siklus I dan siklus II adalah model supervisi klinis. Hasil penelitian menunjukkan bahwa setelah dilaksanakan supervisi menggunakan model supervisi klinis, kemampuan guru dalam membuat soal kemudian di share ke seluruh siswa menggunakan aplikasi kahoot menunjukkan adanya peningkatan, dari siklus I ke siklus II. Siklus II mengakhiri pembinaan, dengan indikator skor guru minimal 80.00 sudah diatas 85%. Kata kunci: Supervisi akademik, kemampuan, Literasi digital
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.028 | 0.012 |
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".