Learning task models in ill-defined domain using an hybrid knowledge discovery framework
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
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发表时间：
2011-02
期刊：
影响因子：
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通讯作者：
R. Nkambou;Philippe Fournier-Viger;E. Nguifo
中科院分区：
文献类型：
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作者：
R. Nkambou;Philippe Fournier-Viger;E. Nguifo
领域专家应该为智能教学系统（ITS）提供相关的领域知识，使其能够在解决问题的学习活动中指导学习者。然而，对于定义不明确的领域，这种知识很难明确定义。我们的假设是，知识发现（KD）技术可以用来提取解决问题的任务模型，从记录使用的专家，中级和新手学习者。本文提出了一个基于序列模式挖掘和关联规则发现技术相结合的过程知识获取框架。该框架已经实现，并用于发现新的元知识和规则，在一个给定的领域，然后扩展领域知识，并作为问题空间，允许智能辅导系统，以指导学习者解决问题的情况。初步实验已经进行了使用该框架作为替代路径规划问题求解器在CanadarmTutor。
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.