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)


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.


user modeling pattern discovery student emotion engagement 


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  1. 1.
    Arroyo, I., Beal, C.R., Murray, T., Walles, R., Park Woolf, B.: Web-Based Intelligent Multimedia Tutoring for High Stakes Achievement Tests. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220, pp. 468–477. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  2. 2.
    Arroyo, I., Mehranian, H., Woolf, B.: Effort-based Tutoring: An Empirical Approach to Intelligent Tutoring. In: Proceedings of the 3rd International Conference on Educational Data Mining, Pittsburgh, PA (2010b)Google Scholar
  3. 3.
    Baker, R.S., Corbett, A.T., Koedinger, K.R., Roll, I.: Detecting When Students Game the System, Across Tutor Subjects and Classroom Cohorts. In: Ardissono, L., Brna, P., Mitrović, A. (eds.) UM 2005. LNCS (LNAI), vol. 3538, pp. 220–224. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  4. 4.
    Chui, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of Knowledge Discovery in Data, pp. 493–498 (2003)Google Scholar
  5. 5.
    Cooper, D., Arroyo, I., Woolf, B.P.: Actionable Affective Processing for Automatic Tutor Interventions. In: Calvo, R.A., D’Mello, S. (eds.) New Perspectives on Affect and Learning Technologies. Springer, New York (in press)Google Scholar
  6. 6.
    D’Mello, S.K., Graesser, A.C.: Automatic Detection of Learner’s Affect from Gross Body Language. Applied Artificial Intelligence 23(2), 123–150 (2009)CrossRefGoogle Scholar
  7. 7.
    Johns, J., Woolf, B.P.: A Dynamic Mixture Model to Detect Student Motivation and Proficiency. In: Proceedings of the National Conference on Artificial Intelligence, p. 163 (2006)Google Scholar
  8. 8.
    Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education 8(1), 30–43 (1997)Google Scholar
  9. 9.
    Lin, J., Keogh, E., Lonardi, S., Patel, P.: Finding motifs in time series. In: Proceedings of the 2nd Workshop on Temporal Data Mining, pp. 53–68 (2002)Google Scholar
  10. 10.
    ManyEyes, (retrieved March 11, 2012)
  11. 11.
    Picard, R.W., Papert, S., Bender, W., Blumberg, B., Breazeal, C., Cavallo, D., Machover, T., Resnick, M., Roy, D., Strohecker, C.: Affective Learning–A Manifesto. BT Technical Journal 2(4), 253–269 (2004)CrossRefGoogle Scholar
  12. 12.
    Shanabrook, D., Cooper, D., Woolf, B.: Identifying High-Level Student Behavior Using Sequence-based Motif Discovery. In: Proceedings of EDM, vol. 200 (2010)Google Scholar

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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