Implementasi Digitalisasi Sekolah dalam Meningkatkan Kualitas Pembelajaran di SD Negeri Bulusari 01 Gandrungmangu
Bibliographic record
Abstract
Penelitian ini bertujuan mengetahui implementasi, hasil dan kendala digitalisasi sekolah dalam meningkatkan kualitas pembelajaran di SD Negeri Bulusari 01 Gandrungmangu. Digitalisasi sekolah mencakup penerapan teknologi informasi dan komunikasi dalam proses pembelajaran, termasuk penggunaan perangkat keras dan lunak. Metodologi dalam penelitian ini adalah deskriptif kualitatif dengan teknik pengumpulan data melalui observasi, wawancara, dan dokumentasi. Hasil penelitian menunjukkan bahwa digitalisasi sekolah telah memberikan hal positif terhadap kualitas pembelajaran, seperti peningkatan motivasi belajar siswa, kemudahan akses materi pembelajaran, dan peningkatan keterampilan baik siswa maupun guru. Penelitian ini juga mengidentifikasi beberapa kendala, antara lain keterbatasan infrastruktur teknologi, kurangnya pelatihan bagi guru, dan keterbatasan akses internet bagi sebagian siswa. Sekolah diharapkan dapat meningkatkan infrastruktur, pelatihan berkelanjutan bagi guru, dan siswa memiliki akses yang memadai terhadap teknologi dan internet. Implementasi digitalisasi yang efektif diharapkan dapat meningkatkan kualitas pembelajaran di SD Negeri Bulusari 01 Gandrungmangu.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 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".