STUDENTS' EPISTEMOLOGICAL BELIEFS IN SOLVING GEOMETRY TRANSLATION PROBLEMS
Bibliographic record
Abstract
The 2018 PISA results show that Indonesian students' numeracy and literacy skills are poor. Epistemological beliefs, beliefs in mathematical knowledge, are one of the factors contributing to poor student literacy skills. This study analyzes epistemological beliefs in solving translation problems of Year 9 students in one of the junior high schools in Manipa Islands, Indonesia. This research employed a descriptive a qualitative approach. The subjects in this study were students who tend to meet the indicators of epistemological beliefs. Data collection techniques were observation, tests, interviews, and documentation. The results showed that students' epistemological beliefs in completing the translation met the requirements. First is being able to solve mathematical problems by taking time, where students needed time to solve the problem. This is because students are not sure that they can solve them. Second, students can solve problems that cannot be solved with simple, step-by-step procedures, where students solve problems using their rules/settlement procedures. The third is understanding essential concepts in mathematics, where students understand and work on translation problems using mathematical concepts well. Fourth, word problems are essential in mathematics, with word problems, students can improve critical thinking skill. Finally, efforts can improve mathematical abilities, where students can try to solve problems to improve their math skills. Efforts include reviewing the lesson and increasing practice questions.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".