KETERAMPILAN BERPIKIR KRITIS: MODEL BRAIN-BASED LEARNING DAN DAN MODEL WHOLE BRAIN TEACHING
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui perbedaan keterampilan berpikir krtitis siswa dengan menggunakan model pembelajaran Brain-Based Learning dan model Whole Brain Teaching pada muatan IPA kelas V SDN 3 Senggreng Kecamatan Sumberpucung. Jenis penelitian ini adalah Pra-Eksperimental Design dengan rancangan The Static Group Pretest-Posttest Design. Sampel yang digunakan adalah seluruh kelas 5A sebagai eksperimen 1 dan kelas 5B sebagai eksperimen 2. Instrumen penelitian yang digunakan berupa tes untuk menguji berpikir kritis siswa. Hasil analisis data menunjukkan bahwa keterampilan berpikir kritis dengan menggunakan model Whole Brain Teaching lebih tinggi dibandingkan menggunakan model Brain-Based Learning. Data yang diperoleh menggunakan analisis Uji-t. Dari hasil Uji-t diketahui bahwa sebesar 2,127 dan 2,122, lebih besar ttabel (> 2,020) dan nilai signifikasi 5% menunjukkan bahwa nilai 0,039 dan 0,040 (< 0,05), oleh karena itu hipotesis alternatif diterima. Dengan demikian terdapat perbedaan model Brain-Based Learning dan model Whole Brain Teaching pada keterampilan berpikir kritis IPA kelas V SDN 3 Senggreng Kecamatan Sumberpucung. Diharapan dengan menggunakan model berbasis otak ini, siswa akan lebih mudah memahami materi dan dapat menyelesaikan permasalahan dalam kehidupan sehari-hari.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".