Data is sometimes seen as something cold and removed from the human element, but in reality, it is a window into that very humanity, and can form an essential foundation for keeping students on track.
As part of a Title III federal grant awarded to Bay Path University, the institution has planned to create a predictive model for traditional undergraduate persistence and another for financial aid warning flags. This is final installment in a series of posts that follow the steps in the CRISP-DM framework on our journey to produce the former.
Some students need more support than others to get their education. In today's Academic Minute, the University of South Florida's Paul Dosal describes how to identify these students early on. Dosal is a professor of Latin American history at USF
Jeff Greene and Matt Bernacki are learning scientists in the UNC-Chapel Hill School of Education. They leverage the data that students create when they use digital resources to help them learn.
Should students be told what the data predict about their chances of success? Corporate leaders in predictive analytics business consider the issue posed by an Inside Higher Ed blogger.