Using machine learning to predict integrating computation into physics courses

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.