Pengaruh Pembelajaran Daring Mahasiswa Dimasa Covid 19 Dalam Tugas Mempelajari Dan Menghafal Ayat-Ayat Alquran
Bibliographic record
Abstract
Pandemi Covid 19 menyebar sejak akhir tahun 2019 hingga kini di beberapa wilayah dengan kasus yang berbeda, terhitung 193 negara berjuang dengan keras untuk melawan covid yang tidak pandang bulu. Wuhan adalah salah satu kota di China sebagai tempat pertama kali virus ditemukan, sebelum virus ini lalu berstatus pandemi. Tentu sangat berpengaruh terhadap sosial ekonomi, khususnya bidang pendidikan, pelaksanaan sistem pembelajaran pada satuan pendidikan mengalami perubahan bentuk operasional yaitu intruksi social distancing, hingga berujung pada himbauan lockdown , sehingga model pembelajaran hampir di seluruh Indonesia dengan Model daring mulai daari lembaga pendidikan hingga Instansi Pemerintah. Penelitian ini menggunakan metode diskrptif kualitatif karena jauh lebih subyektif dalam pembelajaran daring di Universitas Duta Bangsa Surakarta yang diputuskan pembeljaran dari rumah. Subyek terdiri dari 2 dosen dan 2 mahasiswa Universitas Duta Bangsa Surakarta. Dengan aplikasi E-Learning UDB, Zoom, Google Meet, Whatsap, vidio call email dll, walaupun terdapat beberapa kendala diantaranya, sinyal internet, kuota , serta tidak bisa bertanya kepada dosen dengan leluasa.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.009 |
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".