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Learning task models in ill-defined domain using an hybrid knowledge discovery framework
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DOI：10.1016/j.knosys.2010.08.002.png)
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Abstract
En 中文
Domain experts should provide Intelligent Tutoring Systems (ITS) with relevant domain knowledge that enable it to guide the learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. Our hypothesis is that knowledge discovery (KD) techniques can be used to extract problem-solving task models from the recorded usage of expert, intermediate and novice learners. This paper proposes a procedural-knowledge acquisition framework 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.(C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Intelligent Tutoring Systems
Data mining
Knowledge acquisition
Knowledge discovery
Robotics
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