Learning Machines Lab publishes paper on factors predictive of faculty integrating computation into physics courses

  • Feb 20, 2019
  • news-articles

PERL members Nick Young, Grant Allen, John Aiken, Rachel Henderson, and Danny Caballero have published an article in Physical Review Physics Education Research titled Identifying features predictive of faculty 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 faculty responding to the survey included a variety of scales, binary questions, and numerical responses. We then used random forest, a supervised learning technique, to explore the factors that are most predictive of whether a faculty member decides to include computation in their physics courses. We find that experience using computation with students in their research, or lack thereof and various personal beliefs to be most predictive of a faculty member having experience teaching computation. Interestingly, we find demographic and departmental factors to be less useful factors in our model. The results of this study inform future efforts to promote greater integration of computation into the physics curriculum as well as comment on the current state of computational instruction across the United States.