Meningkatkan Hasil Belajar Siswa Dengan Menerapkan Model Case Method Pada Pembelajaran Tematik Di Kelas IV UPT SD Negeri 060870 Medan Timur
Bibliographic record
Abstract
The purpose of this study was to improve student learning outcomes by using the case method learning model in thematic lessons in class IV UPT SD Negeri 060870 Medan Timur. This research was conducted in class IV UPT SD Negeri 060870 Medan Timur which is located at Jalan Gunung Krakatau No.196, Pulo Brayan Darat I, Medan Timur District, Medan City, North Sumatra, 20236. The implementation of this research was in semester I, this research was Class Action (PTK). The research steps involved are planning, acting, observing, reflecting, and evaluating and consist of 2 cycles. The subjects in this study were fourth grade students of UPT SD Negeri 060870 Medan Timur for the Academic Year 2022/2023 with a total of 26 students consisting of 11 boys and 15 girls. The object of this study is the learning outcomes in thematic lessons using the case method learning model in class IV UPT SD Negeri 060870 Medan Timur. From the results of this classroom action research several conclusions were obtained, namely: The use of the case method learning model was able to improve the learning outcomes of class IV UPT SD Negeri 060870 Medan Timur in thematic lessons. The average student learning outcomes increased from 50% to 69% in cycle I to 77% in cycle II. Student activities and student learning outcomes in learning increase and complete.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".