  Each year, roughly 30% of first-year students at US baccalaureate
institutions do not return for their second year and over $9 billion is spent
educating these students. Yet, little quantitative research has analyzed the
causes and possible remedies for student attrition. Here, we describe initial
efforts to model student dropout using the largest known dataset on higher
education attrition, which tracks over 32,500 students' demographics and
transcript records at one of the nation's largest public universities. Our
results highlight several early indicators of student attrition and show that
dropout can be accurately predicted even when predictions are based on a single
term of academic transcript data. These results highlight the potential for
machine learning to have an impact on student retention and success while
pointing to several promising directions for future work.
