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Using Similarity to Infer Meta-cognitive Behaviors During Analogical Problem Solving

  • Kasia Muldner
  • Cristina Conati
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3538)

Abstract

We present a computational framework designed to provide adaptive support aimed at triggering learning from problem-solving activities in the presence of worked-out examples. The key to the framework’s ability to provide this support is a user model that exploits a novel classification of similarity to infer the impact of a particular example on a given student’s metacognitive behaviors and subsequent learning.

Keywords

Normal Force Fact Node Intelligent Tutor System Superficial Similarity Adaptive Support 
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 2005

Authors and Affiliations

  • Kasia Muldner
    • 1
  • Cristina Conati
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

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