Increasing Student Learning Outcomes in Mathematics About Operational Numbers With Role Playing Method of Colors Media in Class V SDN Karanganyar Gunung 02 Semarang
Bibliographic record
Abstract
This study aims to improve the ability to learn mathematics through the media of colored marbles in fifth grade students of SD Karang Anyar Gunung 02 Candisari District, Semarang City. This study uses the Kemmis Taggart class action research method with the stages of planning, implementing, observing and reflecting which consists of two cycles. The subjects of this study were 24 students of class V. Data collection techniques used observation, interviews and documentation. Techniques of data analysis using qualitative analysis. The results of the study showed that out of the 24 students who had completed the pre-cycle, only 11 (45%) students and 13 (55%) students had not completed. In the first cycle showed an increase, namely students who completed there were 16 students or (66.67%) and students who had not completed there were 8 students or (33.33%), then the results of the improvement in learning cycle II showed an increase, namely students who completed learning there were 23 students or (96%) and students who have not finished studying there is 1 student or (4%). Thus the results of the color marbles media method can improve the learning outcomes of fifth grade students at SDN Karanganyar Gunung 02 Semarang in the 2019/2020 academic year.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".