Development of an Experiential Physical Computing Kit for Learning Supervised and Unsupervised Machine Learning

Abstract

With the rapid advancement of AI technologies, including generative AI, artificial intelligence is becoming increasingly pervasive in society and everyday life. In Japan, national initiatives such as the Cabinet Office’s AI Strategy 2022 emphasize the importance of mathematics, data science, and AI education for all citizens, and teacher training materials for the high school subject Informatics II explicitly address unsupervised learning as a core AI mechanism. Unsupervised learning extracts latent patterns and structures from large-scale data without ground-truth labels and is widely used in real-world applications such as big data analytics. However, educational resources that enable high school students to concretely understand its learning process remain insufficient. Prior work has developed physical computing materials using autonomous robots to facilitate understanding of supervised learning, yet hands-on learning tools that deepen students’ understanding of unsupervised learning are still limited. To address this gap, this study extends prior educational designs by integrating unsupervised learning components into an experiential physical computing kit, enabling learners to engage with feature extraction and clustering processes in an intuitive and tangible manner. The developed kit is intended to support learners in forming a concrete understanding of both supervised and unsupervised learning through embodied interaction and data-driven experimentation.

Keywords: Physical Computing, Experiential Learning, Face-to-face Classes

DOI: 10.54941/ahfe1008179