Developing Curriculum for Promoting Design Thinking of Art and Design Students in A Higher Vocational College in Guangzhou, China
Bibliographic record
Abstract
This study aims to develop a curriculum to enhance the design thinking of art and design students in higher vocational education in Guangzhou, China, and assess the effectiveness of its implementation. Collected data using the Design Thinking Scale (DTLS). A total of 31 participants came from majoring in art and design at a higher vocational college in Guangzhou, China. Descriptive statistics were used to analyze the mean and standard deviation of design thinking before and after, and paired sample t-tests were used to detect significant differences between design thinking pretest and post-test. The results showed that participants in the curriculum preferred specific strategies, and there were significant differences between before and after the test. Therefore, it provides evidence that the curriculum developed in this study has potential and feasibility and positively impacts students’ design thinking abilities. This study provides an in-depth curriculum analysis and integrates educational theories, teaching models, and practical perspectives to enhance students’ design thinking abilities. In addition, curriculums that enhance students’ design thinking abilities can provide appropriate guidance for students and professional educators. The results of this study can serve as a teaching medium and method for evaluating students’ design thinking abilities in art and design curriculums.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".