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About

John Black’s research sits at the vital intersection of embodied cognition, robotics education, and early STEM learning. His work demonstrates that young children can grasp abstract scientific and computational concepts not through passive instruction, but by physically interacting with programmable robots. In his most cited work, “Effective Planning Strategy in Robotics Education: An Embodied Approach” (2017, 7 citations), Black argues that embodiment—using one’s own body to simulate or plan robotic movements—is a powerful scaffold for developing planning and programming skills. This builds on his earlier foundational studies, including a case study of an after-school LEGO robotics program (2009, 3 citations) and a pioneering investigation into using LEGO NXT Mindstorms to teach physics to elementary students (2010, 3 citations). In that study, children programmed robots to enact physics concepts like force and motion, transforming abstract theory into tangible, kinesthetic experience. Though his citation counts are modest, Black’s contributions are notable for their pedagogical innovation and practical impact on K-12 STEM curriculum design. His work offers a compelling model for educators seeking to make coding and physics accessible, engaging, and deeply understood by even the youngest learners.