Andreas Lingnau

Ruhr West University of Applied Sciences

Papers

Total Citations

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About

Andreas Lingnau’s research lies at the intersection of educational technology, human-computer interaction, and computer science education, with a particular focus on using robotics as a gateway to deeper learning. His most-cited work, “An Evidence-Based Learner Model for Supporting Activities in Robotics” (2020), addresses a critical classroom challenge: how to provide personalized, scalable support when a single teacher cannot attend to every student during hands-on robotics exercises. Lingnau’s contribution is a learner model grounded in empirical evidence, designed to power intelligent tutoring systems that adapt in real time to students’ actions and misconceptions. This work has garnered 3 citations, reflecting its foundational role in a niche but growing field. Beyond this paper, his broader research agenda explores how interactive learning environments—especially those involving tangible, programmable artifacts—can foster computational thinking and engagement. Lingnau’s approach is notable for its practical orientation: rather than proposing abstract frameworks, he develops deployable systems that address real classroom constraints. For students and researchers interested in the future of adaptive, robot-mediated education, Lingnau’s work offers a clear, evidence-based path forward, demonstrating how learner modeling can transform a chaotic classroom into a supportive, individualized learning experience.