Using Prescriptive Analytics to Reduce Course Dropout (Note: There is no recording for this session.)

Thursday, October 17, 2013 | 9:00AM–9:50AM | Ballroom E
Session Type: Professional Development
Student retention has become a priority in higher education in recent years due to its impact on students, institutions, and society. The session will describe a roadmap for creating a prescriptive, analytics-based system to reduce course dropout. We will discuss predictive and prescriptive analytics techniques and challenges in implementing analytics-based projects.

OUTCOMES:
See how to create a roadmap for prescriptive analytics to identify at-risk students. | Learn statistical and machine-learning techniques used in creating a predictive model. | Learn about the challenges of prescriptive analytics and ways to mitigate them.

Presenters

  • Rajeev Bukralia

    Associate Professor, Minnesota State University, Mankato