Machine Learning for User Modeling
User Modeling and User-Adapted Interaction
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
Enhancing student learning through hypermedia courseware andincorporation of student learning styles
IEEE Transactions on Education
Intelligent assistance for teachers in collaborative e-learning environments
Computers & Education
Machine learning based learner modeling for adaptive web-based learning
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
A survey on concept drift adaptation
ACM Computing Surveys (CSUR)
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We present Adaptive Bayes, an adaptive incremental version of Naïve Bayes, to model a prediction task based on learning styles in the context of an Adaptive Hypermedia Educational System. Since the student's preferences can change over time, this task is related to a problem known as concept drift in the machine learning community. For this class of problems an adaptive predictive model, able to adapt quickly to the user's changes, is desirable. The results from conducted experiments show that Adaptive Bayes seems to be a fine and simple choice for this kind of prediction task in user modeling.