IMPLEMENTASI METODE MAKE A MATCH DALAM PENDEKATAN SAINTIFIKMATA PELAJARAN PKN PADA SISWA KELAS IV SDN KEBONSARI 01 JEMBER
Bibliographic record
Abstract
Pembelajaran dengan pendekatan saintifk memerlukan variasi berbagai metode pembelajaran agar proses pembelajaran lebih bermakna, salah satunya dengan menerapkan metode Make A Match. Implementasi Make A Match dalam Pendekatan saintifik bertujuan untuk membuat siswa lebih aktif dan kritis sehingga berdampak pada hasil belajar. Pada pembelajaran PKN aktivitas dan hasil belajar siswa kategori cukup. Rumusan masalah penelitian ini adalah bagaimanakah implementasi Metode Make A Match dalam Pendekatan Saintifik dapat meningkatkan aktivitas dan hasil belajar siswa. Penelitian ini bertujuan untuk meningkatkan aktivitas dan hasil belajar siswa. Jenis penelitian ini adalah penelitian tindakan kelas dengan 2 siklus tiap siklus meliputi perencanaan, tindakan, observasi dan refleksi. Pengumpulan data menggunakan metode observasi, wawancara, tes, dan dokumen. Hasil penelitian menunjukkan aktivitas pra siklus 48,55%, siklus I 71,44% dan siklus II 92,36%. Hasil belajar afektif siswa pra siklus 60,03%, siklus I 77,13%, dan siklus II 85,69%. Hasil belajar kognitif siswa pra siklus 64,73%,siklus I 73,15%, dan siklus II 83,10%. Hasil belajar psikomotorik siswa pra siklus 65,39%,siklus I 71,38%, dan siklus II 85,85%. Berdasarkan hasil tersebut dapat disimpulkan bahwa implementasi metode make a match dalam pendekatan saintifik sangat efektif dapat dilihat dari adanya peningkatan aktivitas dan hasil belajar siswa kelas IV A di SDN Kebonsari 01 Jember. Hendaknya guru bisa melakukan variasi-variasi metode pembelajaran.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.065 | 0.029 |
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".