Efektivitas Program Peningkatan Pendamping Literasi Perpustakaan Sekolah/Madrasah Tahun 2024: Analisis Pre-Test dan Post-Test
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengevaluasi efektivitas Program Peningkatan Pendamping Literasi Perpustakaan Sekolah/Madrasah Tahun 2024 yang dilaksanakan oleh Perpustakaan Nasional Republik Indonesia. Program ini dirancang untuk meningkatkan kompetensi pendamping literasi melalui pelatihan manajemen perpustakaan, pengembangan program literasi berbasis kurikulum, dan pemanfaatan teknologi informasi. Penelitian menggunakan metode kuantitatif dengan desain pre-test dan post-test one-group design tanpa kelompok kontrol. Data pre-test dan post-test dianalisis menggunakan uji statistik paired sample t-test untuk mengukur perbedaan signifikan antara kemampuan awal dan setelah pelatihan. Hasil penelitian menunjukkan bahwa terdapat perbedaan yang signifikan antara skor pre-test dan post-test, dengan peningkatan rata-rata dari 50,18 menjadi 68,93. Nilai t-Statistik sebesar -35,76 dan p-Value sebesar 2,5468E-209 menunjukkan bahwa perbedaan tersebut signifikan secara statistik. Program ini efektif dalam meningkatkan kompetensi pendamping literasi di berbagai provinsi di Indonesia. Namun, beberapa tantangan dalam pelaksanaan, seperti keterbatasan teknologi dan kendala geografis, masih perlu diperhatikan. Rekomendasi diberikan untuk memperluas cakupan program dan memperkuat sinergi dengan dinas pendidikan setempat agar program dapat berjalan berkelanjutan.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".