Equity and inclusion are important goals for higher education. Data can play a central role in achieving these goals. First, data are essential for probing equity. To provide an example, I will describe the discovery of a pattern of gendered performance differences in large foundational courses, both at Michigan and at an array of other Universities. Data can also help create solutions, as when we test new course designs and develop tools that personalize education.
Over the last year, a group of ten large public research universities have launched the Sloan Equity and Inclusion in STEM Introductory Courses, or SEISMIC project. Dozens of faculty, staff, and students from these institutions are working to connect STEM education research to practice in a national “learning laboratory.” Together, we hope to provide the evidence necessary to motivate change, and find practical ways to make our courses more equitable and inclusive.
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Learning Analytics: using data to understand and improve teaching and learning. We are exploring grading patterns and performance disparities both at Michigan and across the CIC, developing a variety of data driven student support tools like E2Coach through the Digital Innovation Greenhouse, an innovation space for exploring the personalization of education, and launching the NSF funded REBUILD project. REBUILD is an interdisciplinary collaboration, fostering the creation of intergenerational research teams including undergrads, grad students, postdocs, and faculty who will apply a scientific, evidence-based approach to teaching and learning in physics, chemistry, astronomy, biology and math.