Developing the Mathematics Learning Management Model for Improving Creative Thinking In Thailand
Bibliographic record
Abstract
The study purposes were : 1) To study current states and problems of relevant secondary students in developing mathematics learning management model for improving creative thinking, 2) To evaluate the effectiveness of model about : a) efficiency of learning process, b) comparisons of pretest and posttest on creative thinking and achievement of students, and c) comparison of creative thinking and achievement between experimental group and control group. The model was created and implemented with grade eight students of secondary schools, in Thailand, and compared with control group, provided in traditional approach. The research results were : Most of relevant teachers didn’t concentrate in mathematics learning for improving creative thinking, and lacked of using strategies to engage divergent thinking. The model was designed through methodology of R&D, which composed of : 1) principles and theoretical concepts, 2) learning objectives, 3) learning process, 4) social system, 5) principles of response, 6) the support system. Whereas, the activities in learning process consisted of 1) engagement and understanding prior knowledge, 2) encounter problem with thoughtful thinking, 3) analyzing alternative and investigating solutions, 4) modifying of thinking pattern, 5) concluding and evaluating for creative thinking. The findings indicated that effectiveness of model based on achievement score was 76.25%, and based on creative thinking was 61.67%. The average posttest in learning achievement and creative thinking abilities of the experimental group were higher than pretest, and experimental group showed higher of creative thinking than control group at the .01 level of significance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".