SSTAR Lab Launches New College Enrollment Dashboard

The Student Success Through Applied Research Lab (SSTAR Lab) is excited to announce the launch of a new dashboard showing college-going students in Wisconsin! The SSTAR Lab built an interactive map showing high school completion and college-going trends in Wisconsin over the past decade. Combining a powerful mapping tool and time-series data from the Wisconsin Department of Public Instruction (DPI), this Tableau dashboard will be a useful tool for high school counselors and admissions recruiters across the state. Using Tableau, we created county- and school-level maps where users can click on counties and schools to explore their overall high school completion and college-going trends. In particular, we draw attention to the trends for “economically disadvantaged” students, a term used by DPI. We use this variable to identify students who are potentially eligible for the Pell grant.  This can help the Office of Admissions and Recruitment at the University of Wisconsin–Madison recruit more Pell-eligible students. We used two datasets from DPI’s Wisconsin Information System for Education (WISE): High School Completion and Postsecondary Enrollment data from 2009-10 through 2018-19 school years. Although the interactive map is largely descriptive and does not allow you to rank or highlight counties and schools based on college-going rates, we uploaded the data file on the SSTAR Lab’s website to allow others to do so. The request for this project came from our partners in the Office of Student Financial Aid and the Office of Admissions and Recruitment and could not have been completed without their assistance. The SSTAR Lab would like to thank the following people for their assistance: Andrew Hahn and Angela Terrab who provided great feedback and support on the design and development of the Tableau map. Greg Offerman, Jane Duffstein, Renee Koerner, and Kari Temkin who helped us think about how practitioners would use this tool. Carl Frederick at the Wisconsin DPI who helped us understand the data better and improve the quality of the cleaned dataset.