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Machine Learning (ML) opens exciting scientific opportunities in K-12 STEM classrooms. However, students struggle with interpreting ML patterns due to limited data literacy. Face glyphs offer unique benefit by leveraging our brain’s facial feature processing. Yet, they have limitations like lacking contextual information and data biases. To address this, we created three enhanced face glyph visualizations: feature-independent and feature-aligned range views, and the sequential feature inspector. In a study with 25 high school students, feature-aligned range visualization helped contextual analysis, and the sequential feature inspector reduced missing data risks. Face glyphs also benefit the global interpretation of data. 

Despite significant advances in machine learning (ML) applications within science, there is a notable gap in its integration into K-12 education to enhance data literacy and scientific inquiry (SI) skills. To address this gap, we enable K-12 teachers with limited technical expertise to apply ML for pattern discovery and explore how ML can empower educators in teaching SI. We design a web-based tool, ML4SI, for teachers to create ML-supported SI learning activities. This tool can also facilitate collecting data about the interaction between ML techniques and SI learning. A pilot study with three K-12 teachers provides insights to prepare the next generation for the era of big data through ML-supported SI learning. 

The importance of machine learning (ML) in scientific discovery is growing. In order to prepare the next generation for a future dominated by data and artificial intelligence, we need to study how ML can improve K-12 students’ scientific discovery in STEM learning and how to assist K-12 teachers in designing ML-based scientific discovery (SD) learning activities. This study proposes research ideas and provides initial findings on the relationship between different ML components and young learners’ scientific investigation behaviors. Results show that cluster analysis is promising for supporting pattern interpretation and scientific communication behaviors. The levels of cognitive complexity are associated with different ML-powered SD and corresponding learning support is needed. The next steps include a further co-design study between K-12 STEM teachers and ML experts and a plan for collecting and analyzing data to further understand the connection between ML and SD.