Pengaruh Model Direct Intruction Berbantuan Media Patsang terhadap Hasil Belajar Matematika
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh model Direct Instruction berbantuan media PATSANG (Papan Tangga Satuan Panjang) terhadap hasil belajar matematika siswa kelas III di Desa Growong Kecamatan Tempuran Kabupaten Magelang. Penelitian ini merupakan jenis penelitian pre-experimental design dengan model one group pretest posttest design. Subjek penelitian ini menggunakan siswa kelas III di Desa Growong Kecamatan Tempuran Kabupaten Magelang yang berjumlah 10 siswa. Teknik pengambilan sampel menggunakan teknik sampling jenuh. Teknik pengumpulan data berupa tes dalam bentuk pilihan ganda. Uji validitas instrumen tes menggunakan teknik korelasi product moment sedangkan uji reliabilitas menggunakan rumus cronbach alpha dengan bantuan program IMBSPSS versi 24.00 for windows. Uji prasyarat analisis data menggunakan uji normalitas shapiro-wilk dan analisis data yang digunakan yaitu uji paired sample T-test dengan bantuan IMBSPSS versi 24.00 for windows. Hasil penelitian menunjukkan bahwa model Direct Instruction berbantuan media PATSANG (Papan Tangga Satuan Panjang) berpengaruh terhadap hasil belajar matematika. Hal ini dibuktikan dari hasil analisis uji paired sample T-test yaitu nilai sig (2-tailed) 0,000<0,05. Berdasarkan analisis dan pembahasan, terdapat perbedaan nilai rata-rata yaitu 56 sebelum diberikan perlakuan dan 73 setelah diberikan perlakuan. Hasil penelitian membuktikan bahwa model Direct Instruction berbantuan media PATSANG membuat siswa lebih mudah dalam memahami sehingga hasil belajar lebih meningkat.
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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 | 0.006 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.050 | 0.010 |
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".