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The number of ITSs being used daily is growing steadily. Consequently, huge amounts of interaction data are available, but data analysis is still very laborious. This paper describes the use of data mining processes to investigate student interaction with a constraint-based tutor. We discuss how statistical analyses, information visualization and machine learning algorithms can be used to discover interesting patterns in data, and how the findings can be used to improve the system.