Colleges Embrace Data Analytics to Improve Student Retention

As more states adopt funding formulas based on student performance — such as graduation rates and degrees awarded — higher education institutions are ­laser-focused on improving retention. Regardless of state policy, however, such strategies make fiscal sense: Enrolling a new student is more expensive than retaining a current one. To both control those costs and serve students more effectively, many institutions leverage data analytics.

Temple University’s Fox School of Business developed its analytics initiative in concert with a major curriculum review, which included surveying businesses and focus groups about the ­competencies they look for in graduates. The school then looked for a way to help students measure their competencies in an integrated, cross-course fashion.

“We wanted to demonstrate value and return on investment to a student’s degree,” says Cliff Tironi, Fox’s performance analytics manager.

At Temple, it helped that Fox already had extensive data in its learning ­management system (LMS). Using that as a starting point, Fox built an interface, powered by Microsoft Power BI, that helps students identify their strengths and weaknesses. This real-time dashboard, dubbed RoadMap, is also a ­powerful demonstration of applied analytics for future MBA candidates who will enter a data-driven business world.

“It’s a deconstructed and repackaged report card,” says Tironi, who created RoadMap with Fox Associate Vice Dean Christine Kiely. “Students can view their progress in a way they never could with the traditional LMS.” For example, a student can look at the feedback he or she received on a particular skill — say, all courses with a presentation element — rather than rely on a string of letter grades, which reflect classwork in a wide variety of skill sets.

Those insights support more holistic conversations between students and their advisers, Tironi says. Now, instead of simply meeting credit requirements, advisers help students pick classes that develop well-rounded competencies.

Tironi points out that RoadMap doesn’t require professors to change their teaching methods. They already mapped exams to competencies and rubrics, so RoadMap is essentially an invisible layer of analysis, taking data that was latent in the LMS and using it in a new way. By automating the data-gathering process, researchers can spend more time on high-level analyses.

While Temple University takes a holistic approach to students’ development, the University of Nevada, Las Vegas uses course-level corrections to keep students on track. Consider UNLV’s Anatomy and Physiology, an introductory course that enrolls 1,200 students each year — half of whom fail.

In that stark statistic, Matthew Bernacki saw an opportunity. Bernacki, an assistant professor of educational psychology, had conducted extensive research into student achievement at UNLV. He recognized that data analytics could help flag students who are at risk of failing a course so that instructors can provide timely interventions.

“Students really are generating the same data that can inform their own learning practices, if we can provide them with some sort of intervention based upon it,” Bernacki says.