Yui Ono

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About

Yui Ono is a researcher in computing education, with a primary focus on visual programming and its application for elementary school students. Her work explores how different visual programming paradigms—including block-based, flow-based, AR-based, and robot-based environments—can be systematically measured for complexity and effectiveness in early learning contexts. Ono’s most cited paper, “Measuring Complexity in Visual Programming for Elementary School Students” (2024), introduces a framework for evaluating how these diverse interfaces impact young learners’ cognitive load and comprehension. This contribution is significant because it moves beyond simply advocating for visual tools, instead providing educators and curriculum designers with empirical metrics to choose the most appropriate programming environment for specific age groups and learning goals. While her citation count is still growing, Ono’s research is timely, addressing the critical need for evidence-based approaches in K-12 computer science education. Her work bridges human-computer interaction and pedagogy, offering practical insights for making programming accessible without sacrificing conceptual depth. As visual programming continues to expand globally, Ono’s complexity measurement framework stands to influence how schools integrate coding into their curricula.