Efektifitas Strategi Circle of Questions Dalam Memahami Bacaan Pelajaran Bahasa Indonesia Pada Siswa Kelas V MIN Kebonagung
Bibliographic record
Abstract
Penelitian ini merupakan penelitian eksperimen semu (quasi experiment). Populasi dan sampel dalam penelitian ini adalah siswa kelas V MIN Kebonagung yang berjumlah 59 siswa terdiri dari 2 kelas, yaitu kelas VA sebagai kelas kontrol dan kelas VB sebagai kelas eksperimen. Teknik pengambilan sampel yang digunakan dalam penelitian ini yaitu sampel jenuh. Pengumpulan data menggunakan tes, wawancara, dan dokumentasi. Instrumen tes menggunakan jenis tes pilihan ganda yang melalui analisis validitas dan reliabilitas. Teknik analisis data dengan cara uji prasyarat dan uji hipotesis. Uji prasyarat berupa uji normalitas dan homogenitas sedangkan uji hipotesis menggunakan analisis statistika parametrik yaitu uji t. Hasil penelitian menunjukkan bahwa: 1) terdapat perbedaan kemampuan pemahaman membaca antara siswa yang mendapat pembelajaran menggunakan strategi Circle of Questions dengan siswa yang mendapat pembelajaran menggunakan strategi Know-Want to learn-Learned (KWL). Hal ini dibuktikan dari analisis uji t data post test diperoleh nilai Asymp.Sig.(2-tailed) 0,042 < 0,05 sehingga Ha diterima dan H0 ditolak. 2) Hasil uji efektivitas menggunakan uji Gain Score diperoleh Asymp.Sig.(2-tailed) = 0,044 < 0,05, sehingga Ha diterima dan H0 ditolak. Artinya strategi Circle of Questions teruji efektif dalam pembelajaran pemahaman membaca.
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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 | 0.011 | 0.025 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 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".