UPAYA MENINGKATKAN HASIL BELAJAR SISWA KELAS 2A MI MASYHUDIYAH PADA MUATAN MATEMATIKA MELALUI PENERAPAN MODEL PEMBELAJARAN CONTEXTUAL TEACHING AND LEARNING
Bibliographic record
Abstract
This research aims to find out how the application of the contextual teaching and learning model can improve learning outcomes in mathematics content regarding sides, angles and edges of geometric shapes in class 2A MI Masyhudiyah students. This type of research is classroom action research or PTK. This research design uses the Kemmis and Mc. Taggart model which consists of 4 components, namely planning, implementing actions, observing and reflecting. The research subjects that will be used in this classroom action research are 25 students in class 2A MI Mashhudiyah, consisting of 15 male students and 10 female students. Data collection techniques in this research used interviews, observation and tests. This research data analysis technique uses quantitative descriptive. The criteria for success in this classroom action research are based on increasing student learning outcomes in each cycle. The criteria used to see the success of the action are in the form of increasing student learning outcomes. Students are declared to have improved if a minimum percentage of 70% of all students meet the KKM score of 70. The results of the research show that student learning completeness in the pre-test was 8% with an average score of 43.2%, then in cycle 1 it was 56% with an average score -an average of 68.8 and in the second cycle it was 64% with an average value of 69.2 then in the post test of third cycle it was 80% with an average value of 75. From these data it clearly shows that student learning outcomes have increased and indicators of success have been achieved
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.003 |
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".