How One Institution Used Existing Data to Increase Retention by 5% in One Year

It has become very popular in recent years for schools to purchase expensive predictive analytic software platforms to help move the needle on retention. But do these platforms work? Are they worth the money? Can you (without a deep knowledge of statistics) look at your own data and determine which students to focus on? Join us as we tell you how we did just that, and in the process bumped our retention rates by 5% in one year.

The literature on retention and completion abounds with lists of best practices on how to improve the likelihood of a student staying in college and graduating. Most colleges and universities have implemented all the High Impact Practices (AACU, 2008) and attempted every best practice outlined by Astin (1984), Kuh and associates (2005), Tinto (1999), Gardner (1995), Cuseo (2003), and many others. Colleges and universities have spent countless resources on launching learning communities, first-year seminars, early warning systems, bridge programs, tutoring centers, and supplemental instruction. But in the end, schools often experience minimal benefits from all these new programs, targeted initiatives, and integrated experiences. Or, if institutions do experience some positive results, most campuses are unable to isolate which of these initiatives were most responsible for the gains in retention and graduation rates.

Dr.’s Jared Tippets and Eric Kirby were both recently hired at Southern Utah University and were tasked with reversing a 5-year slide in retention and graduation rates. Through this process, they developed their own internal set of predictive data and used this to guide their work throughout the 2015-16 school year. In the end, these efforts paid off with a 5% increase in retention.

The goal of this session is to help institutions pause and think about their student success and retention efforts and whether or not it is truly moving the needle. The presenters will help attendees think about their own data, teach them how it can be used to find at-risk students, and walk them through a worksheet so they are armed with the knowledge they need to go back to campus and dive right into this approach.

Learning Outcomes

This session will provide attendees with:

• insights into how institutions can use existing data to guide their retention and completion work;
• an understanding of which data points might be most predictive on their campuses;
• specific strategies for putting the data to work and making the numbers actionable; and
• an opportunity to reflect on how these ideas might be implemented on their campus.