The power of predictive analytics is in its ability to leverage known data about the student to not just determine which students need assistance but also to determine what challenges a student might face on their path to success.
The purpose of this paper is to encourage a discussion on how we can best serve students facing financial barriers, especially the low-income, minority, and academically at-risk students most prone to drop out. Considering financial barriers not just during the admission and enrollment phase but also during the academic year to refine student aid alongside academic guidance may provide a clearer path to student success.
