KEEFEKTIFAN MODEL QUANTUM TEACHING DAN MODEL SOMATIC AUDIO VISUAL & INTELEGENCY (SAVI) TERHADAP HASIL BELAJAR IPA SISWA KELAS IV SD SE-KECAMATAN KUTOWINANGUN TAHUN AJARAN 2018/2019
Bibliographic record
Abstract
Pendidikan pada abad-21 diperlukan pembelajaran yang inovatif, salah satunya dalam bentuk model pembelajaran Quantum Teaching dan Somatic Audio Visual and Intelegency (SAVI). Tujuan penelitian ini menganalisis efektifitas model pembelajaran Quantum Teaching dan model pembelajaran Somatic Audio Visual and Intelegency (SAVI) terhadap hasil belajar IPA siswa kelas IV SD. Penelitian ini merupakan penelitian eksperimen dengan pendekatan kuantitatif. Populasi penelitian ini adalah seluruh siswa kelas IV SD se-Kecamatan Kutowinangun tahun ajaran 2018/2019. Sampel penelitian dipilih menggunakan metode cluster random sampling. Teknik pengumpulan data menggunakan tes dan observasi. Hasil penelitian ini menunjukkan bahwa nilai effect size kelas Quantum Teaching dan SAVI pada penelitian ini masing-masing 0,533 dan 0,396 yang merupakan nilai effect size sedang. Berdasarkan hasil penelitian tersebut dapat disimpulkan bahwa model Quantum Teaching lebih efektif dibandingkan model SAVI dalam meningkatkan hasil belajar IPA siswa kelas IV Sekolah Dasar se-Kecamatan Kutowinangun tahun ajaran 2018/2019. Model pembelajaran Quantum Teaching lebih sesuai untuk pembelajaran yang berkaitan dengan lingkungan alam sekitar dibandingkan model pemelajaran SAVI.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.079 | 0.018 |
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".