Towards Inferring Sequential-Global Dimension of Learning Styles from Mouse Movement Patterns
AH '08 Proceedings of the 5th international conference on Adaptive Hypermedia and Adaptive Web-Based Systems
A learning style classification mechanism for e-learning
Computers & Education
Automatic detection of learning styles for an e-learning system
Computers & Education
AH-questionnaire: An adaptive hierarchical questionnaire for learning styles
Computers & Education
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
Enhancing the learning experience: preliminary framework for user individual differences
USAB'10 Proceedings of the 6th international conference on HCI in work and learning, life and leisure: workgroup human-computer interaction and usability engineering
A context-aware personalised m-learning application based on m-learning preferences
International Journal of Mobile Learning and Organisation
Tweets reveal more than you know: a learning style analysis on twitter
EC-TEL'12 Proceedings of the 7th European conference on Technology Enhanced Learning
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Detecting the needs of learners is a challenging but essential task to be able to provide adaptivity. In this paper we present a tool that enables learning management systems (LMS) to detect learning styles based on the behavior of learners during an online course. By calculating the learning styles and filling the student model of LMS with such personal data, a basis for adaptivity is provided.