Development of the Mathematical Problem-Solving Ability Using Applied Cooperative Learning and Polya’s Problem-Solving Process for Grade 9 Students
Bibliographic record
Abstract
The purposes of the study were 1) to investigate the effectiveness of the applied cooperative learning and Polya’s problem-solving process on grade 9 students’ mathematical problem-solving ability, 2) to compare grade 9 students’ learning achievement before and after learning through the applied cooperative learning and Polya’s problem-solving process, and 3) to study the students’ satisfaction toward learning through the applied cooperative learning and Polya’s problem-solving process. The participants were 18 grade 9 students in a Thai secondary school selected by the stratified random sampling method. The instruments were 1) an applied cooperative learning and Polya’s problem-solving process learning management, 2) a mathematical problem-solving test, 3) a learning achievement test, and 4) a satisfaction questionnaire. The data were analyzed using percentage, mean score, standard deviation, one-sample t-test, paired-samples t-test, and effectiveness test with the criteria of 70. The results of the study indicate that 1) the learning management designed using the applied cooperative learning and Polya’s problem-solving process was effective in developing students’ mathematic problem-solving ability, 2) the students’ learning achievement of surface area and volume in the posttest was higher, and 3) the students were satisfied with learning with the lesson plans using the applied cooperative learning and Polya’s problem-solving process. The results could be applied in both mathematics classrooms and similar research studi area of mathematics instruction.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".