Retention and Recruitment: Using a Predictive Analytic Model to Build and Implement a Strategic Graduation and Retention Action Plan (Webinar)

Hosted by the Institutional Research and Analytics and the Center for Teaching and Learning.  Presented by Sherry Cox, Jeremiah McKinley, & Glenn Hansen of the University of Oklahoma.  Increasing student retention and graduation rates is a top priority in higher education. Early identification of at risk students for intervention programs or redirection into other degree paths improves retention and graduation rates. Likewise, given the increasing teacher shortage, identifying strong candidates for Teacher Certification programs and graduating prepared future teachers is crucial. The use of predictive analytics provides a promising method in the quest to increase student success at universities and colleges. Our current predictive analytic model utilizes a machine learning algorithm, extreme gradient boosted machine, to identify strong candidates for Teacher Certification programs as well as predicting graduation and program completion. The prediction model, built on historical data, is being applied as a retention and recruitment tool. A strategic graduation and retention action plan, based on the model, is in use by academic advisors and college administrators with current students identified by the model as at-risk for not graduating. This webinar covers the current model and features, application and analysis with active students, the strategic graduation and retention action plan and its implementation and use by academic advisors and college administrators to assist at-risk students, and future directions.   Anyone unable to attend, please send an e-mail to (joanie.hendricks@wright.edu) who will send you the webinar link - which is available after the live webinar.