Discovering Hierarchical Patterns of Students´ Learning Behavior in Intelligent Tutoring Systems

We present a granular approach to discover new and interesting learning behavior patterns of students learning with an intelligent tutoring system. An n-gram analysis is used to model the learning behavior from learning action streams. The regular and irregular learning behavior patterns are obtained from the n-gram analysis model. Then, the n-gram models are clustered into a hierarchy. 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. Our approach is domain independent and able to manage learning behavior uncertainties.