Aplication of The Elaboration Model to Improve Mathematics Learning Outcomes of Class V Elementary Schools
Bibliographic record
Abstract
<p>The purpose of this study was to improve the learning outcomes of class V students in Mathematics learning with the elaboration learning model. The research undertaken was Classroom Action Research through 3 cycles, each cycle consisting of one meeting. The stages of each cycle are planning, implementing, observing, and reflecting. In the first cycle the learning completeness index was 77.78%, in the second cycle and cycle it increased to 88.89%. With the class average value of 77.78 in the first cycle, 87.04 in the second cycle and 87.78 in the third cycle. From this classroom action research, it can be concluded that the application of the elaboration learning model can improve student learning outcomes in learning Mathematics on the scale of SD Negeri Panulisan Barat 01, Dayeuhluhur District, Cilacap Regency.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".