Improvement of Indonesian Learning Results In Vocabulary Materials With The Make A Match Learning Model In Class 2nd Students of Sd Negeri 3 Getas Academic Year 2020/2021
Bibliographic record
Abstract
This study aims to improve understanding of vocabulary and their meanings by applying modeling techniques to grade II students in elementary schools. This study was designed in two cycles, with the research subjects of grade II students of SD N 3 Getas in Kaloran District, Temangggung Regency with a total number of students. 19 students. The research design used was the Classroom Action Research (PTK) spiral model from Kemmis and Taggart which included four stages of research, namely planning, implementing, observing, and reflecting. Students' understanding of the meaning of vocabulary has increased each cycle. Increased understanding of the meaning of students' vocabulary can be seen from the average of the first cycle only 36.8%. While in cycle II the average score increased by 84.21. It was concluded that using the Make a Match type learning model which was carried out in accordance with the learning steps included preparing question and answer cards, dividing students and cards into two parts, mixing the cards. randomly, dividing cards and explaining the learning process, presenting the results of matching cards, making learning conclusions, providing evaluation and closing the learning process in Indonesian subjects can improve learning outcomes for grade 2 students at SD N 3 Getas, Kaloran District, Temanggung Regency
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".