Penerapan Metode Pembelajaran Discovery Learning Untuk Meningkatkan Aktivitas Dan Hasil Belajar Tik Siswa Kelas Viii B Smp Negeri 3 Monta
Bibliographic record
Abstract
Tujuan penelitian ini adalah untuk mengetahui apakah Penerapan Metode Discovery Learning dapat meningkatkan Aktivitas dan Hasil Belajar pada materi perangkat lunak pengolah kata di kelas VIII B SMPN 3 Monta. Penelitian ini merupakan jenis penelitian tindakan kelas atau (Classroom Action Researc). Hasil analisis menunjukkan; (1) Metode discovery learning (belajar penemuan) dapat meningkatkan aktivitas belajar siswa kelas VIII B SMPN 3 Monta Tahun Pelajaran 2021/2022 dengan rata-rata aktivitas belajar siswa pada siklus I adalah 2,39. Pada siklus II 3,15. (2) Metode discovery learning (belajar penemuan) dapat meningkatkan hasil belajar siswa kelas VIII B SMP N 3 Monta Tahun Pelajaran 2016/2017. Ketuntasan yang diperoleh pada siklus I sebesar 76,92%, siklus 92,3%, Hasil ini menunjukkan adanya peningkatan pada tiap-tiap siklus dan tercapainya ketuntasan belajar yang diharapkan.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.032 | 0.010 |
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".