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Abstract Title: Using Machine Learning to Predict Integrating Computation into Physics Courses
Abstract: Computation is a central aspect of 21st century physics practice; it is used to model complicated systems, to simulate impossible experiments, and to analyze mountains of data. Physics departments and their faculty are increasingly recognizing the importance of teaching computation to their students. We recently completed a national survey of faculty in physics departments to understand the state of computational instruction and the factors that underlie that instruction. The data collected from the 1257 faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used supervised learning to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that personal, attitudinal, and departmental factors vary in usefulness for predicting whether faculty include computation in their courses. We will present the least and most predictive personal, attitudinal, and departmental factors.
Abstract Type: Contributed Poster Presentation
Session Time: Poster Session II
Poster Number: B99

Author/Organizer Information

Primary Contact: Nicholas Young
Department of Physics and Astronomy, Michigan State University
567 Wilson Road
East Lansing, MI 48824
and Co-Presenter(s)
Marcos D. Caballero, Department of Physics and Astronomy, Michigan State University, CREATE for STEM Institute, Michigan State University, Department of Physics and Center for Computing in Science Education, University of Oslo