Extracting Student Models for Intelligent Tutoring Systems

John Stamper, Tiffany Barnes, Marvin Croy

Intelligent Tutoring Systems that adapt to an individual student’s needs have been shown to be effective, showing significant improvement in achievement over non-adaptive instruction. The most successful of these systems require the construction of complex cognitive models that are applicable only to a specific tutorial in a specific field, requiring the time of experts to create and test these models on students. In order to achieve the benefits that ITSs provide, we must find a way to simplify their creation. Therefore, we are creating a framework to automate the generation of ITS student models. The goal is to provide a simple way to allow developers of computer-based training to add adaptive capabilities with minimal work while still maintaining the effectiveness of a true Intelligent Tutoring System.

Subjects: 1.3 Computer-Aided Education; 4. Cognitive Modeling

Submitted: Apr 10, 2007


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