Development of Local Wisdom-Based Science Learning Innovation to Promote Creative Problem-solving Skill: Case Study Chessboard Game of Mueang Kung Pottery, Chiang Mai
Bibliographic record
Abstract
The research aimed to 1) study science knowledge in local wisdom of Mueang Kung pottery, Chiang Mai province, 2) develop local wisdom-based science learning innovation to promote creative problem-solving skills, and 3) study satisfaction levels of students to local wisdom-based science learning innovation to promote creative problem-solving skills. The research was action research. The sample in the research consisted of: 1) one village scholar of Mueang Kung village, Hang Dong district, Chiang Mai province; 2) experts of local wisdom learning management accounting for 3 people; and 3) third-year students of Chemistry Departments and fourth-year students of Faculty of Education, Chiang Mai Rajabhat University for the academic year of 2020 accounting for 15 people. Purposive sampling was used. The study results revealed that: 1) From the study on scientific knowledge in local wisdom of Mueang Kung pottery, Chiang Mai province, it was found that in the process of producing Baan Mueang Kung pottery of Chiang Mai province, scientific knowledge has been used to be integrated to develop quality pottery. The knowledge found in the community can be used to design local wisdom-based science learning innovation to promote creative problem-solving skills. 2) From developing local wisdom-based science learning innovation to promote creative problem-solving skills, it was found that the learning innovation developed in the form of Splendor Board Game with components, namely (1) cards in various forms, (2) the board for playing the Board Game and (3) rules of playing and there were results of assessing efficiency of the innovation (E1/E2) at the level: 80.22/81.56. This is deemed to have efficiency in implementation for learning. 3) Regarding satisfaction levels of students to local wisdom-based science learning innovation, it was found that the students had satisfaction levels at the highest level.
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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 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".