Mathematics Metacognitive Skills of Papua’s Students in Solving Mathematics Problems
Bibliographic record
Abstract
This research focuses on the analysis of mathematics metacognitive skills of Papua’s students in School of Indonesian Children in solving mathematics learning problems. The research was conducted to provide a good quality education for Papuan students, so the research start the research from the metacognitive skills especially in mathematics. The respondents of the research are 6 students from grade VII in School of Indonesian Children. Those respondents are represent of higher mathematical ability, medium mathematical ability, and lower mathematical ability. The research used descriptive qualitative research method. Data collection procedures used in this research was in-depth interview and participant observation as well as documents related to metacognitive process in solving the problems in learning mathematics. The in-depth interview was with the respondents, the Principal, the mathematical teacher and the Character Building teacher. For documents related to metacognitive process in solving the problems in learning mathematics such as the result national exam in elementary, the result daily test of math, the result of quiz or homework. In general, the students in Papua were lack of mathematics metacognitive ability such as, lack of cognitive knowledge and cognitive regulation, sometimes they cannot perform the activities that reflects conscious metacognitive such as mathematics problem solving. The research result indicates that the structure metacognitive ability of students in Papua influences their problem solving ability in learning mathematics. This metacognitive ability is also influenced by the fact that it becomes their characteristics background as respondents from Papua.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".