The Development of the Ability to Solve Mathematical Problems and Academic Achievement Decimal Problem of Prathomsuksa6 Students Through Cooperative Learning Management STAD and KWDL Technique
Bibliographic record
Abstract
The research aimed to achieve the following objectives: 1)Assess the effectiveness of cooperative learning management utilizing the STAD technique and KWDL technique among PrathomSuksa6 students in solving decimal problems, with a target of achieving a 75/75 criterion. 2) Enhance the problem-solving abilities of grade 6students in mathematics by implementing cooperative learning management using the combined STAD technique and KWDL technique, compared to a 75 percent criterion. 3) Improve the learning achievement in mathematics of grade 6 students in solving decimal problems through cooperative learning management, employing the STAD technique and KWDL technique, in line with a 75 percent criterion. The research was conducted with a selected group of 33 students from Prathomsuksa6/1, first semester, academic year 2022, at Ban Chiang Yuen School. The research tools employed were: 1) a cooperative learning plan incorporating the STAD and KWDL techniques, 2) a mathematics problem-solving ability test, and 3) a mathematics learning achievement test. Data analysis involved the use of percentage, mean, standard deviation, and efficiency (E1/E2). The findings of the study indicated the following: Cooperative learning management using the STAD technique and KWDL technique exhibited an efficiency of 76.14/75.45, satisfying the 75/75 criterion. The average mathematics score post-intervention was 75.76 percent, surpassing the 75 percent criterion. Mathematics learning achievement, as measured by the average score post-intervention, reached 75.45 percent, fulfilling the 75 percent criterion.
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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 | 0.001 |
| 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 | 0.000 |
| 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".