EdSurge spoke with two University of Michigan (UM) experts about the ways in which learning analytics is helping universities gain a deeper understanding of student behavior, and in turn, improving their outcomes.
The field of learning analytics is growing rapidly, according to Stephanie Teasley, research professor and faculty director of the Learning, Education & Design Lab at UM's School of Information and Tim McKay, professor of physics, astronomy, and education, and faculty director of UM's Digital Innovation Greenhouse.
As in-depth data become more widely accessible, institutions are exploring new ways to make use of all the information available to them. And some of the most promising innovations in the realm of learning analytics involves analyzing how students think and act.
"Now we have access to very rich and very intensive data about learning and have far more sophisticated techniques for investigating that data," Teasley says, "so it provides a new opportunity for an evidence base of what constitutes learning from a theoretical perspective."
According to Teasley, both students and instructors benefit from learning analytics. The data help educators tailor their instruction to student needs and break away from the traditional lecture format, promoting "practices that help students learn more deeply in a way that's accessible to them as individuals."
McKay argues that the new tools can even benefit students even after they graduate. With the help of predictive analytics, educators can help students practice making evidence-based decisions as they navigate their academic careers. This prepares students to make better decisions after they graduate, both at work and in their personal lives, says McKay.
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Such an advantage may also level the academic playing field for students. With predictive analytics, all students have access to a wealth of information that can guide them on the best educational pathways, whether choosing classes or making other important decisions. Students can simply look to others' experiences in forming their own.
Even though predictive analytics can point students and instructors in the best direction, relying on empirical data does not take the exploration out of higher education, both McKay and Teasley stress.
"People often associate predictive analytics with closing doors and I like to think that it's a way to open doors," Teasley says. "It gives you some idea about which paths are open to you and how these lead to the next opportunity. It offers a different view of what could be and not what has to be based on what some data set said about who you are (DeVaney, EdSurge, 8/31).
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