Much of the Northeast is undergoing a demographic shift, with fewer 18–22 year-old students now than in the past. This, coupled with shifting patterns of student behavior such as entering college with joint community college/high school credit, has made predicting enrolled student counts difficult. We have addressed these issues with a series of predictive models tied to business processes that forecast starting one year out with increasing precision as the semester start approaches. The projections are automatically updated and made available to our cabinet that can then use them to set admissions targets so that the college maintains financial stability.
Outcomes: Understand the factors beyond demographic shifts affecting student enrollment at our institutions * Learn how to effectively model these shifts during the course of 12 months and produce increasingly refined estimates of potential student populations * Understand at least one method of clearly communicating these results to key decision makers and support them in data-informed decision-making
Chief Information Officer, University of Rhode Island