The Development of Mathematical Problem-Solving and Reasoning Abilities of Sixth Graders by Organizing Learning Activities Using Open Approach
Bibliographic record
Abstract
The researchers found that sixth-grade students at Traimit Pattana Suksa School had limited problem-solving and mathematical reasoning skills, which was attributed to the way their learning activities were organized by their teachers. The traditional approach did not allow students the freedom to think and practice solving a variety of problems in unconventional ways that mirror everyday life. To address this, the researchers applied an open approach to organizing activities and developed learning activities that fostered problem-solving and mathematical reasoning skills of the students. The goal of the study was to achieve an average score of not less than 70% using action research based on the concepts of Kemmis and McTaggart. Data were collected using a Sub-test at the end of the operating spiral, Mathematical Problem-Solving Ability Test, Math Reasoning Ability Test, and student behavior observation form. The data were analyzed using descriptive statistics, including percentage, mean, and standard deviation. The findings showed that the open approach to organizing activities can effectively develop the mathematical problem-solving and reasoning capabilities of students. The students were able to create work pieces, explain different types of 3D geometric shapes, show how to find the volume of a rectangular shape from given problem situations, and provide reasons to verify their ideas. According to the test results, 13 students (81.25% of the total number of students) had the ability to solve mathematics problems at 70% or higher, and 15 students (93.75% of the total number of students) had mathematical reasoning ability that met the criteria of 70% or higher.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".