Brown Bag Forum
April 7, 2009
Using Visual Models to Identify Student Pathways to College
12:00 PM -  1:00 PM
Harrison Parlor, Lathrop Hall, 1050 University Avenue
This event is free and open to the public.
Justin Ronca
Lead Mathematical Statistician
Community Affiliate
Elizabeth Stransky Vaade
Policy Analyst

A decade’s worth of research shows that first-generation students, socioeconomically disadvantaged students, and racial minorities are far less likely than wealthier, white peers from college-educated families to go on to higher education and complete a degree. Existing literature also provides a great deal of guidance about effective interventions for students at risk of avoiding or failing in higher education. But there has been little progress in developing these findings into a larger framework for integrating K-12 and postsecondary education access and persistence.

Using the federal NELS:88 database, Justin Ronca and Beth Stransky will demonstrate the use of classification and regression tree methodology to create simple, visually interpretable models that identify the most relevant data for predicting student pathways to and through college. Better understanding of these pathways will help institutions allocate scarce resources more effectively in order to provide targeted interventions to promote student success.

Through this work, Ronca and Stransky hope to create policy-relevant statistical models that require no statistical training on the part of the end user. Furthermore, they will include a method for extracting information from federal data and present protocol guidelines for collecting data that help identify when, how, and for whom education professionals should intervene.
 

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