Math Learning Model that Accommodates Cognitive Style to Build Problem-Solving Skills
Bibliographic record
Abstract
The purpose of this research is to develop mathematical learning models that accommodate the cognitive styles reflective vs. impulsive students to build problem-solving skills, quality (valid, practical, and effective). To achieve the target would do research development (development research) and method development that consists of five stages, namely (1) the initial assessment phase, (2) design phase, (3) the stage of realization/construction, (4) stage of the test, evaluation and revision, and (5) the stage of implementation. To assess the quality of math learning model that accommodates cognitive styles reflective of the impulsive vs, used criteria valid, practical and effective. For testing the quality of models, conducted trials in SMP country 5 Tuban. Based on the research results obtained mathematical learning models that accommodate the cognitive style consists of 6 phases. The term phase can be interpreted as measures of learning activities. Phase of this model are: (1) introduction, (2) the representation of mathematical concepts through realistic problems, (3) organizing the students in groups based on cognitive style reflective impulsive vs. (4) discussion of problem solving and presentation, (5) a problem-solving exercise (Evaluation), (6) cover.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".