Location tracking devices are becoming increasingly popular in practice to study movement of customers or track inventory. However, using location tracking devices in education contexts is quite novel. In this paper, we present a robust Bayesian nonparametric mixture model that clusters location data. We successfully apply this model on location data retrieved from two different events using Quuppa, a location tracking system, to identify statistically significant clusters. Implications of this model include identifying "unexpected" clusters during interactive learning activities such as groups of students isolated from other students. As a consequence, further research in behavioral analysis can be done to analyze why such behavior is exhibited.