A Knowledge Discovery Framework for Learning Task Models from User
  Interactions in Intelligent Tutoring Systems
  Domain experts should provide relevant domain knowledge to an Intelligent
Tutoring System (ITS) so that it can guide a learner during problemsolving
learning activities. However, for many ill-defined domains, the domain
knowledge is hard to define explicitly. In previous works, we showed how
sequential pattern mining can be used to extract a partial problem space from
logged user interactions, and how it can support tutoring services during
problem-solving exercises. This article describes an extension of this approach
to extract a problem space that is richer and more adapted for supporting
tutoring services. We combined sequential pattern mining with (1) dimensional
pattern mining (2) time intervals, (3) the automatic clustering of valued
actions and (4) closed sequences mining. Some tutoring services have been
implemented and an experiment has been conducted in a tutoring system.
