Hybrid Recommender Systems: Survey and Experiments
User Modeling and User-Adapted Interaction
Designing a Dynamic Bayesian Network for Modeling Students' Learning Styles
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
ICALT '08 Proceedings of the 2008 Eighth IEEE International Conference on Advanced Learning Technologies
Children's Interactions with Inspectable and Negotiated Learner Models
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Harnessing Learner's Collective Intelligence: A Web2.0 Approach to E-Learning
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Authoring Neuro-fuzzy Tutoring Systems for M and E-Learning
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
An authoring tool for building both mobile adaptable tests and web-based adaptive or classic tests
AH'06 Proceedings of the 4th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
Expert Systems with Applications: An International Journal
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The identification of the best learning style in an Intelligent Tutoring System must be considered essential as part of the success in the teaching process. In many implementations of automatic classifiers finding the right student learning style represents the hardest assignment. The reason is that most of the techniques work using expert groups or a set of questionnaires which define how the learning styles are assigned to students. This paper presents a novel approach for automatic learning styles classification using a Kohonen network. The approach is used by an author tool for building Intelligent Tutoring Systems running under a Web 2.0 collaborative learning platform. The tutoring systems together with the neural network can also be exported to mobile devices. We present different results to the approach working under the author tool.