Computers in Human Behavior
Advanced Adaptivity in Learning Management Systems by Considering Learning Styles
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
A Kohonen Network for Modeling Students' Learning Styles in Web 2.0 Collaborative Learning Systems
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A decision support system to improve e-learning environments
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Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
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
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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
A literature-based method to automatically detect learning styles in learning management systems
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Expert Systems with Applications: An International Journal
International Journal of Learning Technology
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Making students aware of their learning styles and presenting them with learning material that incorporates their individual learning styles has potential to make learning easier for students and increase their learning progress. This paper proposes an automatic approach for identifying learning styles with respect to the Felder-Silverman learning style model by inferring their learning styles from their behaviour during they are learning in an online course. The approach was developed for learning management systems, which are commonly used in e-learning. In order to evaluate the proposed approach, a study with 127 students was performed, comparing the results of the automatic approach with those of a learning style questionnaire. The evaluation yielded good results and demonstrated that the proposed approach is suitable for identifying learning styles. By using the proposed approach, students’ learning styles can be identified automatically and be used for supporting students by considering their individual learning styles.