UDC 681.5.015

Constraint-Based Knowledge Representation for Individualized Instruction

Stellan Ohlsson1 and Antonija Mitrovic2

  1. Department of Psychology, University of Illinois at Chicago
    1007 West Harrison Street, Chicago, IL 60607
    stellan@uic.edu
  2. Intelligent Computer Tutoring Group, Computer Science Department
    University of Canterbury, Private Bag 4800, Christchurch, New Zealand
    tanja@cosc.canterbury.ac.nz

Abstract

Traditional knowledge representations were developed to encode complete, explicit and executable programs, a goal that makes them less than ideal for representing the incomplete and partial knowledge of a student. In this paper, we discuss state constraints, a type of knowledge unit originally invented to explain how people can detect and correct their own errors. Constraint-based student modeling has been implemented in several intelligent tutoring systems (ITS) so far, and the empirical data verifies that students learn while interacting with these systems. Furthermore, learning curves are smooth when plotted in terms of individual constraints, supporting the psychological appropriateness of the representation. We discuss the differences between constraints and other representational formats, the advantages of constraint-based models and the types of domains in which they are likely to be useful.

Publication information

Volume 3, Issue 1 (Jun 2006)
Year of Publication: 2006
ISSN: 1820-0214 (Print) 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Ohlsson, S., Mitrovic, A.: Constraint-Based Knowledge Representation for Individualized Instruction. Computer Science and Information Systems, Vol. 3, No. 1, 1-22. (2006)