This chapter presents an innovative approach to discover granular learning behavior patterns of students learning interactions with an intelligent tutoring system (ITS). The approach is domain independent and able to manage learning behavior uncertainty. An N-gram analysis is used to model the learning behavior from the learning action streams to obtain regular and irregular learning behavior patterns. Then, the N-gram models are clustered into a hierarchy using a rough set-based map granule. The hierarchical pattern can be used to improve the domain knowledge of an ITS in predicting student's actions, sequencing problems to be solved, and adjusting hint mechanisms.