Strategi Pendidikan Dasar untuk Menghadapi Tantangan Era Kurikulum Digital dengan Studi Empiris
Bibliographic record
Abstract
Perkembangan teknologi informasi dan komunikasi telah membawa perubahan paradigma dalam dunia pendidikan. Era kurikulum digital menuntut adanya adaptasi dan strategi yang tepat dalam menyikapi tantangan baru dalam proses pembelajaran di tingkat pendidikan dasar. Penelitian ini bertujuan untuk menganalisis dan mengidentifikasi strategi pendidikan dasar yang efektif dalam menghadapi perubahan kurikulum digital, dengan menggunakan pendekatan studi empiris. Penelitian ini mengadopsi metode kualitatif dengan pendekatan studi empiris. Data dikumpulkan melalui observasi kelas, wawancara dengan guru dan siswa, serta studi dokumentasi terkait implementasi kurikulum digital di sekolah-sekolah dasar. Partisipan penelitian terdiri dari guru-guru dan siswa-siswa dari beberapa sekolah dasar yang telah mengimplementasikan kurikulum digital dalam proses pembelajaran.Penelitian ini memberikan kontribusi penting dalam merumuskan strategi pendidikan dasar yang efektif untuk menghadapi tantangan era kurikulum digital. Hasil temuan ini diharapkan dapat menjadi referensi bagi pihak-pihak terkait dalam memperbaiki sistem pendidikan dasar dan menyongsong masa depan pendidikan yang lebih inovatif dan adaptif dengan perkembangan teknologi
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.032 | 0.005 |
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".