Using design thinking to cultivate the next generation of female STEAM thinkers
Bibliographic record
Abstract
Abstract Background Countries around the world have struggled to implement education policies and practices to encourage more female youths to pursue Science, Technology, Engineering, and Mathematics (STEM). This has resulted in a persistent and sizeable gender gap in science and mathematics subjects in some countries. Using mixed-methods sequential explanatory design, this paper explores an educational intervention—specifically, a 3-day design thinking workshop—in Japan, designed to change female youths’ perceptions regarding STEM topics. Framed using a constructivist approach to learning, the workshops aimed to engender creative confidence, empathy, and global competence among youths. Results The findings show that female youths who participated in the workshop had increased interest in engineering, greater creative confidence, more positive perceptions of STEM, higher levels of empathy and pro-social factors, and a more varied outlook on career options. We argue that this short intervention had a strong influence on the female youths’ mindsets, self-images, and perceptions of STEM. Conclusion This study provides empirical support that a short intervention can produce positive change in how female youths relate to STEM. In gendered societies, an innovative method like design thinking has the potential to revitalize education curriculum in ways that spur female youths’ confidence and creativity, enabling them to imagine a career in the field of STEM.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".