Predicting persistence of women in physics with machine learning

In this work, we use a variety of machine learning tools to predict retention of women in physics. Previously, we used data collected at the Conference for Undergraduate Women in Physics, along with a follow-up survey, to study which factors correlated with long term persistence in physics. The factors we studied were sense of belonging, sense of community, interest, physics identity, perceived recognition, and performance competence. In this study, we build on our previous results by comparing the machine learning methods of support vector machines, neural networks, random forests, and logistic regression to best predict which women are most at risk of leaving the discipline.

The end goal of this study is a tool that can effectively predict whether an undergraduate woman may need more support to remain in physics. Using this, professors could provide targeted interventions to increase overall retention, supporting gender equity in physics. Though we focus on gender equity in this work, the principles of our machine learning approach can be used for other predictive measures in physics education. This work aims to highlight the uses of each machine learning technique for other potential work.