Peningkatan Hasil Belajar Matematika Pada Siswa Sd Melalui Penerapan Model Pembelajaran Kooperatif Tipe Team Assisted Individualization
Bibliographic record
Abstract
Tujuan penelitian ini adalah mendiskripsikan peningkatan hasil belajar matematika siswa kelas IV SD Negeri 14 Teminabuan Distrik Teminabuan Kabupaten Sorong Selatan Papua Barat melalui pembelajaran Kooperatif Tipe Team Assisted Individualization Tahun Pelajaran 2020/ 2021 pada kompetensi menentukan ukuran sudut pada bangun datar. Penelitian ini dilaksanakan sebanyak dua siklus. Masing-masing siklus terdiri dari empat tahap, yaitu: perencanaan (planning), pelaksanaan (action), pengamatan (observation), dan refleksi (reflection). Setiap siklus dilaksanakan sebanyak dua kali pertemuan. Berdasarkan hasil penelitian menunjukkan bahwa hasil belajar matematika tentang menentukan ukuran sudut pada bangun datar cenderung meningkat setelah mendapatkan pembelajaran kooperatif tipe Team Assisted Individualization daripada pembelajaran yang konvensional. Pengukuran peningkatan kualitas proses dilihat dari peningkatan aktifitas belajar siswa pada prasiklus sebesar 52,3% dengan kriteria kurang (D), meningkat pada siklus I sebesar 71%, dan pada siklus II menjadi 85,6% dengan kriteria sangat baik (A). Peningkatan kualitas hasil diukur dari peningakatan daya serap siswa terhadap kompetensi menentukan ukuran sudut pada bangun datar, melalui uji kompetensi. Sebelum dilakukan tindakan ketuntasan klasikal mencapai 41%, dengan rata- rata hasil belajar 64,4. Ketuntasan klasikal pada tindakan siklus I meningkat menjadi 64,7%, dengan rata- rata hasil belajar 71,03, dan pada siklus II ketuntasan klasikal meningkat signifikan sebesar 88%, dengan rata- rata hasil belajar 82,1.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.112 | 0.030 |
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".