UMSI learning analytics project wins $1.25 million in MIDAS Challenge Initiatives grants
UMSI Research Professor Stephanie Teasley was awarded a $1.25 million grant from the Michigan Institute for Data Science (MIDAS) to fund a learning analytics project, which seeks to build a holistic model of student achievement. As principal investigator of the project, Teasley will use data drawn from U-M’s learning technologies and information available in the student data warehouse to build a model of student achievement that will provide tailored instruction for the specific needs of all students.
One of the aims of Teasley's project is to integrate the learning analytics work being done by U-M research in several different fields—from visualizing educational data to studying active learning technologies to analysis of in-class behavior—into a single, data-driven model that promises to yield new insights into the learning process.
"This research has the potential to yield new understandings of how people learn," Teasley said. "Our goal is to demonstrate how data-driven inquiry can improve teaching and learning in higher education."
Associate Professor Kevyn Collins-Thompson and Research Assistant Professor Chris Brooks will also contribute to Teasley’s project along with researchers from the College of Engineering, School of Education and the departments of Physics and Astronomy.
This funding was awarded as part of a U-M Data Science Initiative announced in fall 2015. Four research projects – two each in transportation and learning analytics – received $1.25 million dollars in the first round of the MIDAS Challenge Initiatives program, which was determined through competitive submission process.
Several UMSI professors will also collaborate on MIDAS-funded projects.
Assistant professors Ceren Budak and Tawanna Dillahunt will contribute to a project which aims to design and operate an on-demand, public transportation system for urban areas. The project involves synchronizing a fleet of connected and automated vehicles with bus and light rails, using predictive models based on high volumes of diverse transportation data.
Collins-Thompson will contribute to another project which seeks to uncover connections between students’ personal attributes, such as values, beliefs, interests and goals, and their success in school and overall sense of well-being.
The goal of the multiyear MIDAS Challenge Initiatives program is to foster data science projects that have the potential to prompt new partnerships between U-M, federal research agencies and industry. The challenges are focused on four areas: transportation, learning analytics, social science and health science.
"U-M is in a great position to advance the field of learning analytics because we have a great trove of conventional academic data, other related databases, and an outstanding team of researchers across many disciplines to analyze them," said Alfred Hero, co-director of MIDAS and professor of electrical engineering and computer science.