Using Analytics to Intervene with Underperforming College Students (Innovative Practice)

Session Details

Wednesday, January 20, 2010
11:30 a.m. - 12:30 p.m.
Governor's Ballroom D (fourth floor)

Session Type: Concurrent Session
Content Level: Intermediate


  • John Fritz, AVP, Instructional Technology, University of Maryland, Baltimore County
  • Eric Kunnen, Associate Director of eLearning and Emerging Technologies, Grand Valley State University
  • Session convener: Malcolm Brown, Director, EDUCAUSE Learning Initiative, EDUCAUSE


Data mining is typically associated with business and marketing. For example, Amazon uses people's past purchases to suggest books they might be interested in buying. Similarly, academic analytics can be used to identify and predict students who might be at risk, by analyzing demographic and performance data of former students. However, there is no clear consensus on how to intervene with current students in a way they will accept and not associate with academic "profiling." Why should students think they are exceptions to our rules? This panel presentation will share how three institutions are approaching this problem and provide an overview of related issues.

Available Resources