A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems
MICAI '09 Proceedings of the 8th Mexican International Conference on Artificial Intelligence
Identification of Felder-Silverman learning styles with a supervised neural network
ICIC'10 Proceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing
A framework for creating, training, and testing self-organizing maps for recognizing learning styles
Edutainment'10 Proceedings of the Entertainment for education, and 5th international conference on E-learning and games
Building and assessing intelligent tutoring systems with an e-learning 2.0 authoring system
IBERAMIA'10 Proceedings of the 12th Ibero-American conference on Advances in artificial intelligence
Adaptive e-learning using ECpAA rules, Bayesian models, and group profile and performance data
International Journal of Learning Technology
Expert Systems with Applications: An International Journal
A learning social network with recognition of learning styles using neural networks
MCPR'10 Proceedings of the 2nd Mexican conference on Pattern recognition: Advances in pattern recognition
Expert Systems with Applications: An International Journal
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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When using Learning Object Repositories, it is interesting to have mechanisms to select the more adequate objects for each student. For this kind of adaptation, it is important to have sound models to estimate the relevant features. In this paper we present a student model to account for Learning Styles, based on the model defined by Felder and Sylverman and implemented using Dynamic Bayesian Networks. The model is initialized according to the results obtained by the student in the Index of Learning Styles Questionnaire, and then fine-tuned during the course of the interaction using the bayesian model, The model is then used to classify objects in the repository as appropriate or not for a particular student.