Systematic Literature Review: Application of Problem Solving in the Mathematics Curriculum
Bibliographic record
Abstract
Problem solving is a very important aspect of mathematics education curricula. Each country has a different approach to integrating problem solving into its curriculum. This article discusses a comparison of the application of problem-solving strategies in mathematics curricula in various countries using a Systematic Literature Review (SLR) approach. The results of the study show that the most widely used research method in the studies was qualitative, with 4 articles. Furthermore, these studies used quantitative and mixed methods, with 3 articles each. Articles that met the inclusion criteria showed that Turkey tended to apply problem solving the most in its mathematics curriculum. Next are Finland, Japan, and the Netherlands, followed by South Korea, Vietnam, Singapore, Germany, Canada, and Malaysia. Curricula in various countries shape students' problem-solving skills through different methods but with the same goal: to produce students who are able to think critically and apply mathematics in everyday life.
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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 | 0.010 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".