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Predicting Student Help-Request Behavior in an Intelligent Tutor for Reading

  • Joseph E. Beck
  • Peng Jia
  • June Sison
  • Jack Mostow
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2702)

Abstract

This paper describes our efforts at constructing a fine-grained student model in Project LISTEN’s intelligent tutor for reading. Reading is different from most domains that have been studied in the intelligent tutoring community, and presents unique challenges. Constructing a model of the user from voice input and mouse clicks is difficult, as is constructing a model when there is not a well-defined domain model. We use a database describing student interactions with our tutor to train a classifier that predicts whether students will click on a particular word for help with 83.2% accuracy. We have augmented the classifier with features describing properties of the word’s individual graphemes, and discuss how such knowledge can be used to assess student skills that cannot be directly measured.

Keywords

Request Rate Woodcock Reading Mastery High Grade Level Request Pattern Phonemic Information 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Joseph E. Beck
    • 1
  • Peng Jia
    • 1
  • June Sison
    • 1
  • Jack Mostow
    • 1
  1. 1.Project LISTEN, School of Computer ScienceCarnegie Mellon UniversityPittsburghUSA

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