Relentless data tracking key to MTSU's success
Middle Tennessee State University uses registration data to monitor retention progress
Middle Tennessee State University’s vice provost for student success brings an uncommon perspective to his job, but it is one that is increasingly recognized as having value. Rick Sluder joined the administrative team at MTSU after four years in enrollment management, where it was his job to track application and matriculation data obsessively. The first thing he did when he got to Middle Tennessee State was to set up a data system that would give the university the power to track performance and do so on a weekly basis.
When he hears how other schools approach retention or completion initiatives — measuring progress once per year or less — he says he has to chuckle.
“The places that are doing retention work the best are doing it the most,” Sluder said.
Middle Tennessee State University is part of EAB’s Student Success Collaborative, which claims about 200 members. Their participation gives MTSU staff access to a proprietary predictive model that identifies students who are at-risk, analytics capabilities to track and evaluate targeted campaigns, and a referral system so staff across campus can coordinate student support.
One fundamental way Sluder and his team are using the system is to track pre-registration from semester to semester, looking at persistence as a predictor of retention. The university hired 47 new advisors, bringing the student-to-advisor ratio down to a median of 260:1. Year-over-year comparisons of registration and re-enrollment numbers have become a rallying cry at MTSU, providing goalposts for improvement as advisors and others work to keep students on track.
In just the first few months of this work during the 2014-15 school year, 390 additional students persisted from the fall to the spring semester, generating $1.5 million in additional estimated revenue in tuition and fees. Beyond that, MTSU gets its funding from the state based on performance.
“There’s revenue production and funding that is implied here on all the initiatives that we’ve got,” Sluder said.
So far, the first-year freshman retention rate is 73.2% at MTSU. The goal is to get that up to 80%. Already, traditional achievement gaps have been closed on this metric, with black female students retaining at 78.5%, according to Sluder. For students who participate in the university’s Scholars Academy — nine out of 10 of whom have been black in the six years the program has been running — the freshman retention rate is 85%.
Sluder believes MTSU’s success is replicable. The key is being methodical, strategic, and relentless.
The strategy is part of the art of student success initiatives. Each campus approaches its unique student population differently, using specific filters to identify at-risk students and create tailored supports to best serve them.
Ed Venit, senior director with EAB and one of the leading voices in the development of the firm’s predictive modeling system, can point to 61 different targeted advising campaigns that have been used on campuses across the country. Some identify students with characteristics presenting immediate performance concerns — those with low credit completion ratios for the term, those who have not created a degree plan, those pursuing a selective program but have a GPA below what is required for admittance.
Others target students who represent future performance concerns, like those with GPAs in the “murky middle” for their major or concentration, those with downward trending GPAs, or those whose grades are with .2 points of the GPA requirement for their program.
Still others help advisors find students who may have made the wrong program choice, or are progressing slower than recommended, or who might benefit from extra outreach about the student experience.
While there has been a debate about predictive models running the risk of making advisors too prescriptive, Venit believes people are becoming more comfortable with the idea that tracking data simply gives advisors another tool — it doesn’t change the structure of what advisors do or remove human judgment from the equation.
“This is giving advisors more information when they work with students,” Venit said. “We’re taking data the advisor was looking at anyway and making it more accessible.” Academic records form the core of the EAB modeling system, which finds patterns in student transcripts, grades, and demographic data.
Colleges and universities have jumped on analytics investments in large numbers, especially in the last five years. Increasingly, schools are pairing these tools with customer relationship management software and breaking down silos so students get more coordinated support. And moving forward, Venit says the next frontier will be improving accountability at all levels of an organization, from advisors all the way up to top administrators.
More colleges are entering an era of data-driven decision-making and in this world, success may breed success.
“If you can produce results, you produce that momentum that keeps it going,” Sluder said.
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