The Implementation of the Polya Method in Solving Euclidean Geometry Problems
Bibliographic record
Abstract
This research is aimed at analyzing the solutions of Euclidean Geometry problems using the Polya method. This present study was made through qualitative and quantitative approaches with 85 respondents of the second semester students at the Department of Mathematics Education, University of Muhammadiyah Malang Indonesia, in the 2012/2013 academic year. The quantitative study was made through instruments used to understand students’ responses to the implementation of the Polya method and to know their capabilities in solving two Euclidean Geometry problems. All instruments before being applied were tested for their validity and reliability, and the tests show that the instruments have fulfilled validity and reliability requirements. Qualitative study was made to reinforce the results through interviews to 6 students chosen from those in the low, medium and good levels. The results show that in terms of their understanding of the problems, majority students are good. Dealing with the planning of problems solution, the results show that the majority students made such plans. Then for the carry out the plan, all students did implementation, but for look back, most students did not make any review.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".