Examining Spatial and Spatial Computational Thinking Outcomes in a Technology-Mediated STEM Context: Effects of Race, Gender, and Instructional Format

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  In Roundtable Session: Immersive and Data-Rich STEM Learning Analytics

Abstract

Spatial computational thinking (S-CT) integrates spatial and computational skills to solve complex, data-driven problems in STEM. This study examined how gender, race, and instructional format influence students’ general spatial thinking and S-CT. Using a factorial design, 263 K–12 students engaged in weather data visualization modules using the Integrated Data Viewer, a professional 3D geoscience tool. Modules were delivered in semester-long or intersession formats. No significant gender effects were found, suggesting equitable spatial learning across genders in context-rich, technology-based environments. However, significant effects of race and instructional format emerged: White students showed greater gains in intersession settings, while Non-White students showed limited improvement. Findings underscore the need for inclusive instructional designs that support spatial learning across diverse student populations.

Authors

• Yan Sun, Mississippi State University

• Timothy O Okunoye, Mississippi State University

• Jamie Dyer, Mississippi State University