PENGGUNAAN MODEL MEANINGFUL INTRUCTION DESIGN DALAM MENINGKATKAN PEMBELAJARAN FIQIH BAGI SISWA DI MADRASAH TSANAWIYAH
Bibliographic record
Abstract
ABSTRAK Penelitian ini bertujuan untuk mengimplementasi Model Pembelajaran Meaningful Intruction Design (MID) adapun penelitian ini dilaksanakan dalam 2 siklus. Penelitian ini merupakan penelitian tindakan kelas. Penelitian ini terdiri dari 4 alur yaitu: (1) Perencanaan, (2) Tindakan, (3)Pengamatan, (4) Refleksi. Subjek dalam penelitian ini adalah Guru Fiqih dan siswa kelas VII.2 yang terdiri dari 34 Orang. Instrumen penelitian ini, yaitu observasi, tes, wawancara, dan dokumentasi. Hasil penelitian menunjukan adanya peningkatan persentase ketuntasan hasil belajar Fiqih pada setiap siklus, pada siklus 1 persentase ketuntasan belajar siswa mencapai 50%, dengan jumlah siswa 17 orang yang mencapai KKM dengan nilai rata-rata 60, dan setelah dilaksanakan siklus 2 persentase meningkat menjadi 91% dengan jumlah siswa 31 orang yang mencapai KKM dengan rata-rata nilai 77. Hal ini membuktikan bahwa model pembelajaran Meaningful Intruction Design (MID) bisa meningkatkan hasil belajar Fiqih kelas VII.2 Madrasah Tsanawiyah Swasta Penyengat Olak Provinsi Jambi. Kata Kunci: Model Meaningful Intruction Design, Hasil Belajar, Fiqih
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".