Penerapan Model Pembelajaran Kooperatif Tipe Group Investigation Dalam Meningkatkan Hasil Belajar Materi Sifat-Sifat Cahaya Siswa
Bibliographic record
Abstract
This research examines the Applying Of Cooperative Learning Model Type Of Group Investigation To Increase Students Achievement Topic The Nature Of Light Grade IV UPTD SD Negeri 58 Parepare. The problem of this research are 1) How can the applying of cooperative learning model type of group investigation can increase student process topic the nature of light grade IV UPTD SD Negeri 58 Parepare? 2) Can the applying of cooperative learning model type of group investigation to increase achievement students topic the nature of light grade IV UPTD SD Negeri 58 Parepare?. The type of research used is Classroom Action Research (CAR) and the approach used is a qualitative approach. The stages of classroom action research consist of 4 stages, namely planning, implementation, observation and reflection. The subjects in this research were fourth grade students of UPTD SD Negeri 58 Parepare for the academic year 2021/2022, totaling 16 students, as well as a teacher. Based on the data obtained during the implementation of the first cycle and second cycle, the results of the research for the first cycle of student learning outcomes with a completeness percentage of 73.43% were in the sufficient category (C). Meanwhile, for the second cycle, the percentage of completeness was 83.37% which was in the good category (B). From this research, it can be concluded that the process and student achievement Topic The Nature Of Light Grade IV UPTD SD Negeri 58 Parepare by applying the group investigation type cooperative learning model has increased.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.007 |
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".