MENINGKATKAN HASIL BELAJAR MATEMATIKA MENGGUNAKAN MODEL PROBLEM BASED LEARNING PADA PESERTA DIDIK KELAS IV SD
Bibliographic record
Abstract
Penelitian ini didasarkan pada menerapkan model-model. Hal ini menyebabkan proses dan hasil belajar peserta didik yang buruk. Penelitian tindakan kelas ini bertujuan untuk meningkatkan hasil dan proses belajar matematika di kelas IV SDN 34/II Leban. Penelitian ini terdiri dari dua siklus, dengan perencanaan, pelaksanaan, observasi, dan refleksi. Studi ini dimulai pada semester kedua akademik 2022/2023. Metode pengumpulan datanya adalah melalui pengamatan, dokumentasi, dan hasil tes. Hasil analisis data penelitian menunjukkan bahwa model pembelajaran berbasis (PBL) dapat meningkatkan proses dan hasil belajar matematika di kelas IV SDN 34/II Leban. Hal ini ditunjukkan oleh hasil proses mengajar guru pada siklus I sebesar 74% dan siklus II sebesar 98%, dengan peningkatan pada siklus I dan II sebesar 24%. Hasil proses belajar peserta didik pada siklus I sebesar 64,22% dan siklus II sebesar 90,74%, dengan peningkatan pada siklus I dan II sebesar 26,5%. Hasil belajar peserta didik pada siklus I sebesar 45,45% dan siklus II sebesar 72,72%, dengan peningkatan pada siklus I dan II sebesar 45.75%.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".