Utilizing Integrated Think-Pair-Share and SSCS Techniques to Enhance Problem-Solving Skills in Mathematical Set among 10th Grade Students
Bibliographic record
Abstract
This study aimed to investigate the impact of integrating Think-Pair-Share (TPS) and Search Solve Create Share (SSCS) techniques on the problem-solving skills of 10th-grade students within the framework of set theory, while also assessing participants' satisfaction with these methods. Conducted using a one-group experimental research design, the study involved 37 10th-grade students selected through cluster random sampling from a public school in Mahasarakham province, Thailand. Ethical considerations were meticulously observed throughout the study. Instruments utilized included a TPS and SSCS learning management plan, a Mathematics Set Problem Solving Ability Test, a Satisfaction Questionnaire, and Rubric Scoring. Data analysis employed percentage, mean score, standard deviation, and a one-sample t-test comparing to a predetermined criterion of 75% of the maximum score for each test. The results demonstrated that the integrated TPS and SSCS techniques were effective in developing students' problem-solving abilities and provided satisfying learning experiences. This underscores the efficacy of collaborative learning techniques, particularly in enhancing problem-solving skills. Additionally, the study highlights the potential of integrating various collaborative methods to achieve favorable outcomes in mathematics education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".