Visualization of Student Activity Patterns within Intelligent Tutoring Systems

  • David Hilton Shanabrook
  • Ivon Arroyo
  • Beverly Park Woolf
  • Winslow Burleson
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7315)

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.

Keywords

user modeling pattern discovery student emotion engagement 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • David Hilton Shanabrook
    • 1
  • Ivon Arroyo
    • 1
  • Beverly Park Woolf
    • 1
  • Winslow Burleson
    • 2
  1. 1.Department of Computer ScienceUniversity of MassachusettsAmherstUSA
  2. 2.School of Computer Science and InformaticsArizona State UniversityUSA

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