Article thumbnail
Location of Repository

Problem-Solving Knowledge Mining from Users’\ud Actions in an Intelligent Tutoring System

By Roger Nkambou, Engelbert Mephu, Olivier Couturier and Philippe Fournier-Viger


In an intelligent tutoring system (ITS), the domain expert should provide\ud relevant domain knowledge to the tutor so that it will be able to guide the\ud learner during problem solving. However, in several domains, this knowledge is\ud not predetermined and should be captured or learned from expert users as well as\ud intermediate and novice users. Our hypothesis is that, knowledge discovery (KD)\ud techniques can help to build this domain intelligence in ITS. This paper proposes\ud a framework to capture problem-solving knowledge using a promising approach\ud of data and knowledge discovery based on a combination of sequential pattern\ud mining and association rules discovery techniques. The framework has been implemented\ud and is used to discover new meta knowledge and rules in a given domain\ud which then extend domain knowledge and serve as problem space allowing\ud the intelligent tutoring system to guide learners in problem-solving situations.\ud Preliminary experiments have been conducted using the framework as an alternative\ud to a path-planning problem solver in CanadarmTutor

Topics: Domain knowledge, data mining, Intelligent Tutoring Systems, Knowledge Discovery
Publisher: Springer-Verlag
Year: 2007
OAI identifier:

Suggested articles


  1. (2005). A new informative generic base of association rules",
  2. (2003). A Programming by Demonstration Authoring Tool for ModelTracing Tutors”. In, Authoring Tools for Advanced Technology Learning Environments: Toward Cost-Effective Adaptive,
  3. (2001). A single-query bi-directional probabilistic roadmap planner with lazy collision checking”. Int.
  4. (2005). Anytime Dynamic A*: An Anytime Replanning Algorithme”. doi
  5. (2004). Applying Machine Learning Techniques to Rule Generation in Intelligent Tutoring Systems”. Intelligent Tutoring Systems doi
  6. (2006). Automatic Recognition of Learner Groups in Exploratory Learning Environments”. Intelligent Tutoring Systems
  7. (2004). Bootstrapping Novice Data: Semi-Automated Tutor Authoring Using Student Log Files”.
  8. (2006). Forming Heterogeneous Groups for Intelligent Collaborative Learning Systems with Ant Colony Optimization”. Intelligent Tutoring Systems
  9. (2004). Han et al (2004). Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach. doi
  10. (1995). Mining Sequential Patterns.
  11. (2005). Path-Planning for Autonomous Training on Robot Manipulators in Space”. IJCAI
  12. (2001). SPADE: An Efficient Algorithm for Mining Frequent Sequences.
  13. (2003). The advantages of Explicity Representing Problem Spaces”. User Modeling,
  14. (2006). The Cognitive Tutor Authoring Tools (CTAT): Preliminary evaluation of efficiency gains”. Intelligent Tutoring Systems
  15. (1998). The PSP Approach for Mining Sequential Patterns”.

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.