Empowering Teachers’ Learning to Develop Students’ Design Thinking Skills
Bibliographic record
Abstract
The purpose of this research was to employ the Research and Development (R&D) methodology to produce an educational innovation, called “Online Self-Training Program for Empowering Teachers’ Learning to Develop Students’ Design Thinking Skills”, expected to be widely used among the schools that are the target population in disseminating the research results. This online self-training program consisted of two projects: 1) the development project for teachers' learning, consisting of seven self-training modules for teachers' learning, and 2) the project for teachers using learning outcomes for student development, consisting of a self-training module used as teachers’ practice guideline. The experimental results of the first project revealed that the post-test scores of 16 teachers met the standard criteria of 90/90 and their post-test scores were significantly higher than the pre-test scores. The experimental results of the second project pointed out that the post-test scores of 300 students were significantly higher than their pre-test scores. The research results were, therefore, by the established research hypotheses, indicating that the effectiveness of the educational innovation was confirmed. As a result, it can be used to benefit teachers and students in schools that are the target population for disseminating research results.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".