U Michigan MIDAS Program Backs Student Success Research

• By Dian Schaffhauser
• 06/23/16

The University of Michigan is self-funding two new research projects that explore student success. One will examine the ties among students' personal attributes, such as values, beliefs, interests, behaviors and backgrounds and their success in school or overall sense of well-being. The other will attempt to build a holistic model of student success through students' written work, behavioral data and institutional data.

Both multi-disciplinary projects will receive$1.25 million in funding from the university's MIDAS program. The Michigan Institute for Data Science (MIDAS) runs a challenge initiatives program that encourages data scientists to work with subject matter experts to tackle problems in learning analytics, transportation, personalized medicine and health, and social science.

The personal attributes project is being led by Rada Mihalcea, a professor of electrical engineering and computer science. Collaborators will come from the college of engineering, school of information, school of public health, school of education, and department of statistics.

In a prepared statement, Mihalcea said the goal of her research is to expand the traditional analysis of educational success, which tends to focus on academic behavior, to include student life, personality and background outside of the classroom.

"Our project envisions using new ways of analyzing data to consider all aspects of a student's experience, lifestyle and background when developing tools to aid in educational success — of course, with due concern and assurances regarding the protection of students' privacy," she noted.

The other project is being led by Stephanie Teasley, a research professor in the school of information, collaborating with researchers from her own school, as well as the college of engineering, school of education and the departments of physics and astronomy. The research team intends to build a model of student achievement that uses multiple statistical and "big data" methods to build a fuller picture of students.

One of Teasley's goals is to pull together learning analytics work being done within several areas of the university into a single model to gain new insights into the learning process.

"This research has the potential to yield new understandings of how people learn," she said. "Our goal is to demonstrate how data-driven inquiry can improve teaching and learning in higher education."

"These interdisciplinary projects will result in new ways of helping students achieve their full potential," added MIDAS co-director Brian Athey, professor and chair of computational medicine and bioinformatics. "The projects will also advance the state of the art in data science, helping to address privacy issues and other important methodological concerns."