Teaching Machine Learning Fundamentals with LEGO Robotics
This work addresses making machine learning accessible and engaging for young learners through a tangible, visualization-based approach, though it is incremental as it applies existing methods to a new educational context.
The paper tackles teaching machine learning to students aged 12-17 by developing a web-based platform using LEGO robotics, resulting in significant improvements in conceptual understanding, positive AI perception shifts, high usability, and increased motivation among 14 students.
This paper presents the web-based platform Machine Learning with Bricks and an accompanying two-day course designed to teach machine learning concepts to students aged 12 to 17 through programming-free robotics activities. Machine Learning with Bricks is an open source platform and combines interactive visualizations with LEGO robotics to teach three core algorithms: KNN, linear regression, and Q-learning. Students learn by collecting data, training models, and interacting with robots via a web-based interface. Pre- and post-surveys with 14 students demonstrate significant improvements in conceptual understanding of machine learning algorithms, positive shifts in AI perception, high platform usability, and increased motivation for continued learning. This work demonstrates that tangible, visualization-based approaches can make machine learning concepts accessible and engaging for young learners while maintaining technical depth. The platform is freely available at https://learning-and-dynamics.github.io/ml-with-bricks/, with video tutorials guiding students through the experiments at https://youtube.com/playlist?list=PLx1grFu4zAcwfKKJZ1Ux4LwRqaePCOA2J.