Developing Creative Problem-Solving Skills of Pre-Service Teachers Through the Go Game During Activities Outside the Classroom
Bibliographic record
Abstract
This research aims to compare creative problem-solving skills and study the creative problem-solving process of learners before and after playing Go Game during activities outside the classroom. The sample group was 21 undergraduate students from the Faculty of Humanities and Social Sciences, Phetchaburi Rajabhat University who registered in the second semester of 2024. The statistics that were used for analyzing data consisted of Means, Standard Deviations, and Dependent Sample t-test. The research results indicated that creative problem-solving skills of learners who played Go game during activities outside the classroom were evidently higher than before playing Go game during activities outside the classroom at .01, statistically. Creative problem-solving skills of the learners after the experiment was still significantly high with the higher scores. Regarding the difference of score levels, this indicated that all sub-skills in the creative problem-solving process of the experimental group showed positive development. All sub-skills possessed higher score level. The skills of selecting problem-solving methods and testing hypotheses had the highest increase in scores. This suggests that playing Go during activities outside the classroom can significantly develop learners’ creative problem-solving skills.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".