Bridging Art and Science: Engaging Girls in the Physics of Sound Through a Transdisciplinary STEAM Approach
Bibliographic record
Abstract
This study explores how adopting a transdisciplinary STEAM (Science, Technology, Engineering, Arts, and Mathematics) approach can deepen elementary-aged girls’ engagement with science by integrating artistic expression and scientific inquiry. Building on a shift from a multidisciplinary model—where disciplines remain adjacent—to a transdisciplinary one—where they are interwoven—the research investigates the impact of a one-day workshop for Grade 3 and 4 students centered on sound graph analysis. Framed by the 5E Instructional Model and guided by a focused ethnography methodology, the workshop invited students to interpret and represent sound visually, creating an embodied and intuitive bridge between art and science. Findings reveal that this seamless integration promoted higher engagement, particularly among girls, by connecting abstract scientific concepts to their personal and sensory experiences. The transdisciplinary framework fostered both analytical and creative thinking, validating students’ diverse modes of understanding. This approach not only made science more accessible but also challenged traditional gendered perceptions of STEM fields. The study underscores the potential of transdisciplinary STEAM education to create more inclusive learning environments and to empower young girls to see themselves as capable science learners through the lens of creativity and everyday relevance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".