Visualization of student activity patterns within intelligent tutoring systems

David Hilton Shanabrook, Ivon Arroyo, Beverly Park Woolf, Winslow Burleson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish (US)
Title of host publicationIntelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings
Pages46-51
Number of pages6
Volume7315 LNCS
DOIs
Publication statusPublished - 2012
Event11th International Conference on Intelligent Tutoring Systems, ITS 2012 - Chania, Crete, Greece
Duration: Jun 14 2012Jun 18 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7315 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other11th International Conference on Intelligent Tutoring Systems, ITS 2012
CountryGreece
CityChania, Crete
Period6/14/126/18/12

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Keywords

  • engagement
  • pattern discovery
  • student emotion
  • user modeling

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Shanabrook, D. H., Arroyo, I., Woolf, B. P., & Burleson, W. (2012). Visualization of student activity patterns within intelligent tutoring systems. In Intelligent Tutoring Systems - 11th International Conference, ITS 2012, Proceedings (Vol. 7315 LNCS, pp. 46-51). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7315 LNCS). https://doi.org/10.1007/978-3-642-30950-2_6