David H. Shanabrook

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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 motifs, that(More)
We have started exploring and facilitating natural collaborations between students sitting in contiguous computers. We implemented a mechanism to facilitate this kind of social interactions, instead of struggling against it. By allowing students to "force" specific problems they want, one student can pull up the math activity that another student is going(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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