We describe our use of an innovative analytic technique adapted for use with student
educational data for the first time: topological data analysis via the Mapper algorithm. In this
approach, students are clustered based on their similarity and a filter which partitions them
into subsamples for iterative clustering. This results in a two-dimensional representation of a
higher space showing both how students cluster together and how those clusters are related
to each other. By using a variety of attitudinal constructs to inform the process, we limit the a …