Using machine learning to predict integrating computation into physics courses

POSTER

Abstract

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. 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.

Authors

  • Nicholas Young

    Michigan State University

  • Paul Irving

    National Superconducting Cyclotron Laboratory and Department of Physics and Astronomy, Michigan State University, East Lansing, MI 48824, USA, Univ of Michigan - Ann Arbor, 15611860790, Michigan State University, Western Michigan University, Kent State University, University of St Andrews, University of Baghdad, King Abdullah University of Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu, China, Bowling Green State University, Bowling Green Ohio, USA, Bowling Green State Univ, Air Force Research Laboratory Sensors Directorate, Saginaw Valley State University Physics Department