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An Adaptive Predictive Model for Student Modeling
Abstract
This chapter presents an adaptive predictive model for a student modeling prediction task in the context of an adaptive educational hypermedia system (AEHS). The task, that consists in determining what kind of learning resources are more appropriate to a particular learning style, presents two issues that are critical. The first is related to the uncertainty of the information about the student’s learning style acquired by psychometric instruments. The second is related to the changes over time of the student’s preferences (concept drift). To approach this task, we propose a probabilistic adaptive predictive model that includes a method to handle concept drift based on statistical quality control. We claim that our approach is able to adapt quickly to changes in the student’s preferences and that it should be successfully used in similar user modeling prediction tasks, where uncertainty and concept drift are presented.
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