Abstract
Domain experts should provide relevant knowledge to a tutoring system so that it can guide a learner during problem-solving learning activities. However, for ill-defined domains this knowledge is hard to define explicitly. As an alternative, this paper presents a framework to learn relevant knowledge related to procedural tasks from users’ solutions in an ill-defined procedural domain. The proposed framework is based on a combination of sequential pattern mining and association rules discovery. The resulting knowledge base allows the tutoring system to guide learners in problem-solving situations. Preliminary experiments have been conducted in CanadarmTutor.
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Nkambou, R., Mephu Nguifo, E., Fournier-Viger, P. (2008). Using Knowledge Discovery Techniques to Support Tutoring in an Ill-Defined Domain. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds) Intelligent Tutoring Systems. ITS 2008. Lecture Notes in Computer Science, vol 5091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69132-7_43
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DOI: https://doi.org/10.1007/978-3-540-69132-7_43
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