Predictive Analytics: The Crystal Ball for Education

Higher Education

By Brittany Becker

Enrollment in Pennsylvania and Ohio postsecondary institutions declined approximately 1.9% and 0.5% in the spring of 2017 compared to the spring of 2016, respectively.  This is consistent with the overall total enrollment in all sectors declining approximately 1.5%.  (According to the National Student Clearinghouse Research Center “Current Term Enrollment Estimates – Spring 2017). 

Enrollment and ultimate retention of students drive the overall budget and performance of all educational institutions.  Wouldn’t it be nice to have a crystal ball that would predict how many students will enroll and matriculate at a certain institution?  Recently, I attended a presentation by Othot that discussed big data analytics that would assist in doing just that.  This “crystal ball” is predictive analytics that educational institutions of all levels can utilize to better understand what factors impact a student’s decision to attend an institution and ultimately what factors may impact their ultimate retention.  Data that has been collected for years and with the assistance of predictive analytics can now can be a valuable tool in determining and planning for the future. 

As institutions grapple with a challenging enrollment environment, these tools can assist in determining which groups of students to pursue or allocate institutional aid dollars to for a desired outcome.  The models provided can be highly precise based on the data provided and can help in developing budgets, providing support to make decisions on class sizes, hiring and facility needs as well as improving methods of marketing to potential students and responding to the needs of current students. 

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