Giving Compass’ Take:
• James Paterson shares how 11 schools used predictive analytics to improve their data sharing program in different ways.
• How could predictive analytics improve data sharing in organizations you work with?
• Learn more about predictive analytics strategies for schools.
When the leaders of 11 prominent public research universities signed an agreement to form the University Innovation Alliance (UIA) in 2014, it was viewed as a fairly bold (if brazen) move. They were going to share ideas in an increasingly competitive higher ed environment to tackle the thorny issue of broadening access.
Their first big initiative as a group was maybe even edgier. They were going to work toward an aggressive goal of expanding admissions by using predictive analytics at a time when few people outside of the information technology field knew much about it. They hoped it would help them enroll and graduate more underserved students.
Colleges form consortia for a variety of purposes, from cost-cutting to helping students find meaningful employment while in school. Lately, the focus has been on keeping up with sophisticated trends in technology development and online learning.
Predictive analytics, in particular, has drawn attention for its potential to help colleges spot trends in their data. The results can help them improve enrollment practices, identify at-risk and struggling students, and streamline advising. Yet it has been slow to gain converts, with fewer than half of colleges seeing it as a priority, according to an October 2016 report from nonpartisan think tank New America. Some also question its ethics, and whether the data delivers biases that cause institutions to limit some students’ options.
Iowa State University used the EAB platform to give academic advisers a new dashboard to track students’ progress based on historical performance benchmarks, said Ann Marie VanDerZanden, associate provost for academic programs. It also provides a system to schedule and document advising sessions and the use of academic supports such as tutoring.
“Centralized scheduling of advising appointments (have) been a huge success for both students and advisors,” she said. In the first 18 months, more than 70,000 advising appointments were scheduled and more than 40,000 advising reports were created using the system. She noted that 58,000 advising sessions were made in the fall semester alone. This year the university added success markers that students are expected to meet, and it placed more attention on student life outside of the classroom.
Michigan State is similarly using the platform to gather data. Through proactive advising in the fall of 2017, it found nearly 430 students who needed additional support between their second and third fall semesters. By being able to identify and reach them more easily, the university could hold advising sessions with them at nearly twice the rate as with other students.
The University of Kansas also used its UIA involvement to focus on student success, said DeAngela Burns-Wallace, vice provost for undergraduate studies. This past fall, the university introduced its comprehensive Jayhawk GPS system, which is based on the EAB platform. Advisers can use it to develop “campaigns,” which identify a group of students with a common concern — for example, they haven’t registered for a necessary course — contact them directly and track their responses.
The University of Central Florida broadened its use of the EAB platform, using historical data to help advisers spot struggling students, contact them and develop academic pathways forward. It has reported small but steady gains in first-year retention (90.4% in 2017-18) and its six-year graduation rate (72.6% in 2012-13).
Beyond using data for interventions, participating colleges have found the process of examining student success and the barriers to it useful.
Amy Martin, assistant dean for undergraduate student success operations at Michigan State, said the mapping process spurred small changes like simplifying and consolidating multiple student calendars, as well as broad shifts like the formation of a campuswide committee that meets regularly to discuss student success initiatives.
Read the full article about sharing data using predictive analytics by James Paterson at Education Dive.
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