SmileyCluster

Abstract

There is an increasing need to prepare young learners to be Artificial Intelligence (AI) capable for the future workforce and everyday life. Machine Learning (ML), as an integral subfield of AI, has become the new engine that revolutionizes practices of knowledge discovery. Making ML experience accessible to young learners, however, remains challenging due to its high demand for mathematical and computational skills. This research focuses on designing novel learning environments that help demystify ML technologies for K-12 students, and also investigating new opportunities for maximizing ML accessibility through integration with scientific discovery in STEM education. We developed SmileyCluster - a hands-on and collaborative learning environment that utilizes glyph-based data visualization and superposition comparative visualization to assist learning an entry-level ML technology, namely k-means clustering. Findings from an initial case study with high school students in a pre-college summer program show that SmileyCluster leads to positive change in learning ML concepts, methods and sense-making of patterns. Findings of this study also shed light on understanding ML as a data-enabled approach to support evidence-based scientific discovery in K-12 STEM education.