Now we can create an actual predictive model to identify these students who were at risk before they ever even came to the college.

—Mary McLean-Scanlon ,Director of Institutional Effectiveness,Finger Lakes Community College

Business Challenge story

Finger Lakes Community College wanted to increase student retention rates. How could it identify students at risk of dropping out and provide targeted support to help them finish their education?


Using an advanced data mining solution with IBM SPSS Modeler and IBM SPSS Statistics, the college identified factors that make it more likely for students to drop out, using this insight to provide mentoring and monitoring to those most at risk.


10 percentage point increase in retention rate for pre-nursing students; Focuses limited resources on helping students most at risk.  Proactive use of information to intercept issues early.

Solution Category

  • IBM Hybrid Cloud
  • Solution Components