Data Science ARES: Sarah Mayes-Tang
Join us at the Data Science Applied Research and Education Seminar (ARES) with:
Assistant Professor, Teaching Stream
Department of Mathematics
University of Toronto
Free Event | Registration Required
Talk Title: Designing creative courses with students in mind
The students that we teach have far different needs than they did when most of our traditional math and statistics courses were initially added to the curriculum. We may also endeavour to reach a broader range of learners – ones who would normally never take a quantitative class, perhaps due to their high school background or negative experiences with math. What non-traditional courses can we offer to these 21st century learners? What are the best practices for designing courses in areas that we may feel like learners too?
In this talk I’ll share my experience creating and teaching courses with titles such as Women’s Math, Creativity in Math, and Math and Literature, intended for students who otherwise would not engage in university-level math. I’ll also discuss ways that I’ve reimagined traditional mathematics courses, including recasting a generic “Introduction to abstract mathematics” as Mathematical Masterpieces.
Sarah Mayes-Tang is an Assistant Professor, Teaching Stream in the Department of Mathematics. She recently ended a four-year term as the coordinator of the University’s largest calculus program. As coordinator she transformed the curriculum and teaching of the course to respond to research on effectively teaching calculus, including improving equity in STEM, and to respond to the needs of the client disciplines. Prior to coming to UofT Sarah was a professor at Quest, a small liberal arts University that reimagined what education would look like if it was rebuilt from the ground up. Her time there was instrumental in developing her approach towards teaching, which respects students’ knowledge, is informed by other disciplines, and rethinks long-held assumptions about math teaching. Sarah holds a PhD in pure math from the University of Michigan. Her research studies asymptotic patterns in collections of polynomial ideals and various issues related to teaching and learning.