Student retention is critical to the success of both students and institutions. Cognitell has developed accurate predictive models of student retention in collaboration with the Wyoming community colleges using data from student information systems (SIS) and learning management systems (LMS). In pursuit of the goal of providing accurate predictions early in the semester, we are developing new student surveys using research-based success factors. Combining SIS, LMS, and survey data sources and engineering population relevant features sets up our modeling for even greater success. We will also show how this framework can be utilized for a prescriptive action recommendation engine designed to suggest interventions for students identified at risk via the model.
