Recently, student retention has surfaced as a priority for many academic institutions. While institutions regard retaining students as important, little has been done to create a system that helps institutions “flag potential defectors” prior to leaving. By identifying “at risk” students, intervention steps can be taken to reduce the likelihood of defecting. The author proposes that institutions develop data mining procedures, similar to those used in business, to identify potential defectors. This data should include both “hard” and “soft” predictors of student defection. An added benefit of this data is that it can be used by institutional advancement, once students become alumni, to improve fundraising efforts by enabling the development of “one-to-one” fundraising/marketing programs.
Data Mining Approach to the Identification of At-Risk Students