Visualization of student activity patterns within intelligent tutoring systems

Authors:
David Hilton Shanabrook;Ivon Arroyo;Beverly Park Woolf;Winslow Burleson
Affiliations:
Department of Computer Science, University of Massachusetts, Amherst;Department of Computer Science, University of Massachusetts, Amherst;Department of Computer Science, University of Massachusetts, Amherst;School of Computer Science and Informatics, Arizona State University
Venue:
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Year:
2012

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Abstract

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 combination with sensor predictors, we demonstrate that a combination of features, automatic pattern discovery and feature selection algorithms can predict and trace higher-level states (emotion) and inform more effective real-time tutor interventions.