PENGARUH MODEL PEMBELAJARAN KOOPERATIF TIPE JIGSAW DAN KEMANDIRIAN TERHADAP HASIL BELAJAR KOGNITIF MATEMATIKA SISWA
Bibliographic record
Abstract
Permasalahan terkait rendahnya hasil belajar kognitif matematika peserta didik juga ditemui di SD Negeri 2 Wana, Kecamatan Kecamatan Melinting Kabupaten Lampung Timur. Berdasarkan data yang diperoleh melalui observasi pra-penelitian masih terdapat banyak sekali peserta didik yang belum mencapai Kriteria Ketuntasan Minimum (KKM) mata pelajaran Matematika yakni 65. Penelitian ini bertujuan untuk mengetahui pengaruh pembelajaran kooperatif tipe Jigsaw terhadap hasil terhadap hasil belajar kognitif peserta didik, mengetahui pengaruh kemandirian terhadap hasil belajar kognitif peserta didik dan mengetahui interaksi model pembelajaran kooperatif tipe Jigsaw dan kemandirian terhadap hasil belajar kognitif peserta didik. Penelitian ini merupakan penelitian yang menggunakan pendekatan kuantitatif, dengan teknik pengambilan data yang peneliti pilih menggunakan teknik purposive sampling dan instrumen yang digunakan pada penelitian ini menggunakan lembar test, angket dan dokumentasi. Berdasarkan hasil penelitian pada Tabel 4.8 di atas dimana model pembelajaran terhadap hasil belajar didapat nilai sig sebesar 0,000 dimana ini kurang dari 0,05 maka terdapat pengaruh. Hasil uji ANOVA dimana kemandirian terhadap hasil belajar mendapat nilai sig sebesar 0,024 dimana ini kurang dari 0,05 maka terdapat pengaruh. Interaksi model pembelajaran dan kemandirian terhadap hasil belajar dimana mendapat nilai sig kurang dari 0,05 yakni 0,028 maka tedapat interaksi yang positif.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".