ML4STEM Professional Development Program: Enriching K-12 STEM Teaching with Machine Learning
Jingwan Tang, Xiaofei Zhou, Xiaoyu Wan, Michael J. Daley, Zhen Bai · International Journal of Artificial Intelligence in Education · 2022
The advances of machine learning (ML) in scientific discovery (SD) reveal exciting opportunities to utilize it as a cross-cutting tool for inquiry-based learning in K-12 STEM classrooms. There are, however, limited efforts on providing teachers with sufficient knowledge and skills to integrate ML into teaching. Our study addresses this gap by proposing a professional development (PD) program named ML4STEM. Based on existing research on supporting teacher learning in innovative technology integration, ML4STEM is composed of Teachers-as-Learners and Teachers-as-Designers sessions. It integrates an accessible ML learning platform designed for students with limited math and computing skills. We implemented this PD program and evaluated its effectiveness with 18 K-12 STEM teachers. Findings confirm that ML4STEM successfully develops teachers’ understanding of teaching STEM with ML as well as fosters positive attitudes toward applying the ML as an in-class teaching technology. Discussions on the implications of our findings from ML4STEM are provided for future PD researchers and designers.