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Record W4411274018 · doi:10.31756/jrsmte.4120si

Bridging Art and Science: Engaging Girls in the Physics of Sound Through a Transdisciplinary STEAM Approach

2025· article· en· W4411274018 on OpenAlexaff
Narges Mansouri

Bibliographic record

VenueJournal of Research in Science Mathematics and Technology Education · 2025
Typearticle
Languageen
FieldPsychology
TopicCreativity in Education and Neuroscience
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsBridging (networking)Sound (geography)Visual artsMathematics educationPhysicsArtComputer scienceAcousticsPsychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.007
Science and technology studies0.0000.005
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.111
GPT teacher head0.499
Teacher spread0.389 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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