Revisiting Ill-Definedness and the Consequences for ITSs

  • Authors:

  • Antonija Mitrovic;Amali Weerasinghe

  • Affiliations:

  • Intelligent Computer Tutoring Group, University of Canterbury, Private Bag 4800, Christchurch, New Zealand, tanja.mitrovic@canterbury.ac.nz, amali.weerasinghe@pg.canterbury.ac.nz;Intelligent Computer Tutoring Group, University of Canterbury, Private Bag 4800, Christchurch, New Zealand, tanja.mitrovic@canterbury.ac.nz, amali.weerasinghe@pg.canterbury.ac.nz

  • Venue:
  • Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
  • Year:
  • 2009

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Abstract

ITSs for ill-defined domains have attracted a lot of attention recently, which is well-deserved, as such ITSs are hard to develop. The first step towards such ITSs is reaching a wide agreement about the terminology used in the area. In this paper, we discuss the two important dimensions of ill-definedness: the domain and the instructional task. By the domain we assume declarative domain knowledge, or the domain theory, while the instructional task is the task the student is learning, in terms of problem-solving skills. It is possible to have a well-defined domain and still have ill-defined instructional tasks in the same domain. We look deeper at the features of ill-defined tasks, which all contribute to their ill/well defined nature. The paper discusses model-tracing and constraint-based modeling, in terms of their suitability for ill-defined tasks and domains. We show that constraint-based modeling can be used in both well-and illdefined domains, and illustrate our conclusion using several instructional tasks.