David H. Shanabrook

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2 Abstract. We describe a data mining technique for the discovery of student behavior patterns while using a tutoring system. Student actions are logged during tutor sessions. The actions are categorized, binned and symbolized. The resulting symbols are arranged sequentially, and examined by a motif discovery algorithm to detect repetitive patterns, or(More)
Novel and simplified methods for determining low-level states of student behavior and predicting affective states enable tutors to better respond to students. The Many Eyes Word Tree graphics is used to understand and analyze sequential patterns of student states, categorizing raw quantitative indicators into a limited number of discrete sates. Used in(More)
Touch is a new and significantly different method of interacting with a computer and it is being adapted at a rapidly increasing rate with the introduction of the tablet computer. We log the characteristics of a student's touch interaction while solving math problems on a tablet. By correlating this data to high and low effort problem solving conditions we(More)
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