Principal Investigator: Zhen Bai
CoPrincipal Investigator(s): Raffaella Borasi, Jiebo Luo, Michael Daley
Organization: University of Rochester
NSF Award Information: EAGER: Cultivating Scientific Mindsets in the Machine Learning Era
Artificial Intelligence (AI) is woven into the fabric of everyday life. There is currently an enormous skills gap in AI for the future workforce. Limited AI learning opportunities among K-12 students and professional development opportunities for teachers may lead to AI inequity in the workforce and education. This project addresses these challenges by introducing Machine Learning (ML) as a discovery tool for data-driven scientific inquiry in K-12 STEM classroom. The project focuses on creating and studying a novel programming-free and visual-based ML-powered learning environment. It aims to enable high school students and teachers with limited mathematical, programming and data skills to discover complex scientific phenomena and ask big questions from thought-provoking patterns hidden in real-world data. Researchers will include high school students from marginalized backgrounds in STEM throughout the research activities and engage in outreach in collaboration with the David T. Kearns Center for Diversity and Leadership at the University of Rochester. This project will contribute to NSF’s missions on promoting inclusion in next-generation STEM education, and advance K-12 AI literacy as a driving force of national prosperity.
Researchers will carry out three synergistic research activities: (1) creating a ML-powered visual learning environment that utilizes a combination of novel glyph-based data visualization and analogical learning process to mitigate the steep learning curve of ML and multi-dimensional pattern discovery for high school learners; (2) adopting a co-design approach to include K-12 STEM teachers and data science experts in creating ML-powered scientific inquiry activities; and (3) iteratively evaluating the effectiveness of the new learning environment in supporting three key learning goals for high school students: multi-dimensional pattern discovery, ML concepts and methods around clustering and classification, and pattern-inspired scientific inquiry through question asking, hypothesis generation, explanation and argument. Findings of this project will advance our knowledge on the design and pedagogical guidelines of ML-powered visual learning environments that minimize cognitive load for novice K-12 AI learners. The resulting novel learning environment, and ML-powered scientific inquiry activities will be made publicly available.
This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.