Peningkatan Prestasi Belajar PKn melalui Metode Kooperatif Pada Siswa SD
Bibliographic record
Abstract
Abstract. The formulation of the problem in this study is whether the cooperative method can be used to improve PKn learning achievement for grade IV students of SD Negeri 2 Nanggulan, District of Cawas, Klaten Regency, second semester 2017/2018 academic year? The goal to be achieved is to prove that the cooperative method can be used to improve PKn learning achievement of grade IV students of SD Negeri 2 Nanggulan, District of Cawas, Klaten Regency, second semester of the school year 2017/2018. This research was conducted at Nanggulan Cawas Klaten Public Elementary School 2. While the time of the study was conducted from February to March 2018. The subjects of this study were grade IV students of SD Negeri 2 Nanggulan Cawas Klaten in the second semester of the school year 2017/2018. This action research uses the classroom action research flow. The data analysis technique used uses several stages, namely data reduction, presentation and conclusion. From the results of the research carried out since planning, repairs to implementation and evaluation, it can be concluded that the cooperative method can be used to improve PKn learning achievement of fourth grade students of SDN 2 Nanggulan, District of Cawas, Klaten Regency, second semester 2017/2018 school year. This was proven before the improvement in the percentage of students' completeness in learning was only 27.78% or 5 students, in the improvement of the first cycle to 55.6% (of 18 students, which was not completed 8 students) and in the second cycle the percentage of student mastery reached 94, 44%. Of the 18 students, 17 of them have achieved mastery learning, while the one student is still incomplete.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.006 |
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".