Empowering Students to Learn Programming Fluency and Computational Thinking via VEX AIM

Framework

This research is grounded in the learning sciences, emphasizing evidence-based design and alignment with national computer science and STEM standards. Computational thinking serves as the guiding framework, with attention to core practices such as abstraction, decomposition, and algorithmic reasoning. The study applies design-based research to evaluate authentic learning outcomes in classroom contexts.

Methods

This study follows a mixed-methods design approved by our Institutional Review Board. Participants include middle and high school students engaged in programming with VEX AIM. A research build of VEXcode collects de-identified student data including coding attempts, task completion rates, and time-on-task, adhering strictly to IRB requirements. Additional data sources include teacher observations, student surveys, and semi-structured interviews. Quantitative data are analyzed for growth in computational thinking and programming fluency, while qualitative responses are coded thematically to capture student perspectives and contextual factors. Triangulation of data sources strengthens validity and enables replication.

Results

Preliminary results suggest students using VEX AIM demonstrate measurable gains in programming fluency, logical reasoning, and computational thinking. We expect to see increased student engagement, persistence, and confidence as they tackle authentic challenges. Teacher observations and student reflections are anticipated to confirm growth in problem solving and collaboration. Final results will highlight both quantitative improvement and qualitative insights that inform best practices for integrating VEX AIM into diverse classrooms.

Importance

This study advances understanding of how students build programming fluency and computational thinking. By combining classroom-based data with teacher and student perspectives, it offers evidence on effective integration of robotics and coding. Findings provide educators and researchers with actionable strategies that support equity, authentic learning, and measurable outcomes in computer science education.

References

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