SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
The PSP Approach for Mining Sequential Patterns
PKDD '98 Proceedings of the Second European Symposium on Principles of Data Mining and Knowledge Discovery
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Path-planning for autonomous training on robot manipulators in space
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
The advantages of explicitly representing problem spaces
UM'03 Proceedings of the 9th international conference on User modeling
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
The cognitive tutor authoring tools (CTAT): preliminary evaluation of efficiency gains
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Automatic recognition of learner groups in exploratory learning environments
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Student procedural knowledge inference through item response theory
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
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In an intelligent tutoring system (its), the domain expert should provide relevant domain knowledge to the tutor so that it will be able to guide the learner during problem solving. However, in several domains, this knowledge is not predetermined and should be captured or learned from expert users as well as intermediate and novice users. Our hypothesis is that, knowledge discovery (kd) techniques can help to build this domain intelligence in ITS. This paper proposes a framework to capture problem-solving knowledge using a promising approach of data and knowledge discovery based on a combination of sequential pattern mining and association rules discovery techniques. The framework has been implemented and is used to discover new meta knowledge and rules in a given domain which then extend domain knowledge and serve as problem space allowing the intelligent tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted using the framework as an alternative to a path-planning problem solver in CanadarmTutor.