  Science, technology, engineering, and math (STEM) fields play growing roles
in national and international economies by driving innovation and generating
high salary jobs. Yet, the US is lagging behind other highly industrialized
nations in terms of STEM education and training. Furthermore, many economic
forecasts predict a rising shortage of domestic STEM-trained professions in the
US for years to come. One potential solution to this deficit is to decrease the
rates at which students leave STEM-related fields in higher education, as
currently over half of all students intending to graduate with a STEM degree
eventually attrite. However, little quantitative research at scale has looked
at causes of STEM attrition, let alone the use of machine learning to examine
how well this phenomenon can be predicted. In this paper, we detail our efforts
to model and predict dropout from STEM fields using one of the largest known
datasets used for research on students at a traditional campus setting. Our
results suggest that attrition from STEM fields can be accurately predicted
with data that is routinely collected at universities using only information on
students' first academic year. We also propose a method to model student STEM
intentions for each academic term to better understand the timing of STEM
attrition events. We believe these results show great promise in using machine
learning to improve STEM retention in traditional and non-traditional campus
settings.
