Pelaksanaan Supervisi Akademik Pada Masa Pandemi Covid-19 Di Sekolah Dasar
Bibliographic record
Abstract
Pandemi Covid-19 menyebabkan terjadinya kedaruratan di segala bidang. Kedaruratan di bidang pendidikan ditandai dengan kebijakan pelaksanaan pembelajaran jarak jauh (PJJ). Penelitian ini bertujuan mendeskripsikan pelaksanaan supervisi akademik pada masa pandemi Covid-19 di SDIT Aljabar Gondang. Metode penelitian yang digunakan adalah kualitatif. Teknik pengumpulan data menggunakan observasi, wawancara, dan studi dokumentasi. Visitasi kelas untuk observasi pembelajaran tidak dapat dilaksanakan, dikarenakan proses pembelajarannya dengan cara pembelajaran jarak jauh (PJJ). Karena itu visitasi kelas diganti dengan visitasi pembelajaran kelas virtual. Instrumen yang digunakan adalah google form. Kepala sekolah mengirim instrumen kepada guru yang disupervisi melalui google form, selanjutnya guru tinggal mengisi. Instrumen visitasi kelas virtual mengacu pada instrumen visitasi kelas dalam pembelajaran normal, tetapi redaksinya diedit menyesuaikan kebutuhan untuk mendapatkan informasi tentang pelaksanakan PJJ. Berdasarkan vitasi kelas virtual tersebut diketahui bahwa dalam masa pandemi Covid-19 para guru SDIT Al Jabar Gondang tetap melaksanakan tugasnya, yaitu dengan cara blanded learning. Pembelajaran jarak jauh (PJJ) secara on line yang dicampur dengan pembelajaran tatap muka (PTM) terbatas secara bergiliran. PJJ dilaksanakan untuk menyampaikan materi pembelajaran yang esensial, sedangkan PTM terbatas untuk pengumpulan tugas dan pemberian bimbingan khusus yang tidak dapat dilaksanakan secara efektif melalui PJJ
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.066 | 0.031 |
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".