Introducing HelioCampus Playbooks for Retention Modeling

Resources

• Meeting recording (UMBC login req'd)
• Anonymous evaluation

Over the past several months, the Data Science and Business Intelligence teams have been working with HelioCampus to develop a First and Second year student retention model for First Time, Full Time Freshman. This model’s aim is to assess the likelihood that a first year or second year student, who entered as a first time freshman, will continue to be enrolled through the following fall. 

The predictive model uses a number of current and historical academic metrics, as well as various demographic, geographic, and financial features to develop these predictions. The scoring provided by this model are based on the preceding spring semester. In conjunction with Academic Affairs, this model is a useful tool to plan interventions for students at risk of attrition. 

In this session, DoIT's Robert Carpenter and Len Mancini will lead a demo & discussion about the importance of predictive scores for each student using a series of dashboards that describe the risks associated with a particular score and how the model comes to that decision.