Identifying features predictive of faculty integrating computation into physics courses

TL;DR

This study uses machine learning to identify key factors influencing physics faculty's integration of computation into their courses, highlighting personal experience and beliefs over demographic factors.

Contribution

It applies Random Forest analysis to survey data to uncover predictors of computational instruction among physics faculty, providing insights for curriculum development.

Findings

Experience with student computation predicts faculty inclusion of computation.

Personal beliefs significantly influence computational teaching decisions.

Demographic and departmental factors are less predictive.

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.