New Summit!

What: University Data Summit
When: August 23, 2017: All day
Where: The iHotel Chancellor Ballroom (tentatively)
Who:

  1. Anyone who maintains or provides campus data to the University of Illinois community
  2. Anyone who is interested in improving program, student and financial outcomes
  3. Anyone interested in applications of data science

Synopsis: The University of Illinois acquires, archives, and supports access to a data that touches all of campus including students, alumni, faculty and staff, facilities, research, outreach, and alumni and corporate engagement. Unfortunately, these data are often siloed, each with their own restrictions that severely limits access to individual data and generally prevents federation and value-added analysis. 

With the growth of data science, our peers have demonstrated that these data hold great promise to revolutionize how Universities operate. As a result, we are holding a University Data Summit to publicize the different types of data available within our university, to discuss regulations that limit their utility, and to brainstorm about tools and analyses that relevant stakeholders would like to see developed. As a specific example, just as companies spend a considerable amount of money to improve targeted ads in online settings, we could use a similar approach to aid in recruitment of a diverse and inclusive student body, to identify and assist at-risk students earlier in the process, and to more effectively tailor alumni engagement.

Specific outcomes of the summit might include designs of new tools and related data sets to enable the following:

  1. Develop a student “academic health” record (in a similar spirit to medical records, but focused on academics) that encode a student’s academic life from admissions through graduation and into alumni relations. This record can be used: 
    • by admissions personnel to increase admission yields, 
    • by advisors and faculty to improve retention and graduation rates and 
    • by advancement to tailor alumni engagement on an individual basis.
  2. With aggregated student records, we can develop machine learning models to better: 
    • better predict (and increase) admissions yields to positively impact program rankings
    • identify and recruit students from under-represented groups to increase diversity
    • identify and support at-risk students earlier.
  3. With aggregated student and alumni data we can develop network models to improve interactions with potential donors so that all information about their previous familial experiences with the University of Illinois including connections to current students or other alumni are known by all engaged campus representatives.

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