Speaker
Description
This talk describes the design and features of a newly developed undergraduate course in numerical linear algebra and an accompanying instructor-led research study that examines how students engage with theoretical structure and computational behavior in AI-supported environments.
This course offered an introduction to numerical linear algebra, focusing on its theoretical foundations, practical applications, and computational techniques. Topics included matrix factorization (LU, QR, Cholesky, SVD), eigenvalues and eigenvectors, iterative methods, and least squares. Computational explorations in MATLAB were central to the course, allowing students to learn theories through computation and real-world applications, bridging the numerical and abstract linear algebra.
The instructor conducted educational research by collecting students’ work, regular reflections, and an exit survey, along with the instructor’s daily teaching reflections, to document classroom interactions and instructional decisions. The goal is to examine how students interpret numerical output, reason about algorithmic reliability, and incorporate AI-assisted tools while remaining grounded in theory. Some preliminary data and analysis will be shared in this talk.
The course was designed and created in collaboration with Mike Michailidis and Jon Loftin from MathWorks.