Predicting Student Dropout in Higher Education
This addresses student retention issues for universities, but it is incremental as it applies existing methods to new data.
The study tackled the problem of student dropout in higher education by modeling it using a large dataset of over 32,500 students, showing that dropout can be accurately predicted based on a single term of academic transcript data.
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.