A KST-BASED SYSTEM FOR STUDENT TUTORING
Applied Artificial Intelligence
Learning path generation by domain ontology transformation
AI*IA'05 Proceedings of the 9th conference on Advances in Artificial Intelligence
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Knowledge space theory was already applied for building adaptive testing and training systems which contain test and training problems including their prerequisite structure, and which offer explanations and help for solving these problems. Normally, however, lessons prepare students for solving problems.In this paper, we present a method for systematically structuring an adaptive eLearning course containing test problems as well as lessons. This method is based on knowledge space theory extended by component-wise representation of problems and on applying demand analysis. A course developed with such a method can be fed into an adaptive tutoring system, realized, e.g., within the adaptive tutoring system RATH.