Cluster Analysis for Users' Modeling in Intelligent E-Learning Systems
IEA/AIE '08 Proceedings of the 21st international conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems: New Frontiers in Applied Artificial Intelligence
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
Learning teamwork skills in university programming courses
Computers & Education
Experience Structuring Factors Affecting Learning in Family Visits to Museums
EC-TEL '09 Proceedings of the 4th European Conference on Technology Enhanced Learning: Learning in the Synergy of Multiple Disciplines
Student Groups Modeling by Integrating Cluster Representation and Association Rules Mining
SOFSEM '10 Proceedings of the 36th Conference on Current Trends in Theory and Practice of Computer Science
The foundations of a theory-aware authoring tool for CSCL design
Computers & Education
AH-questionnaire: An adaptive hierarchical questionnaire for learning styles
Computers & Education
Studying the impact of personality and group formation on learner performance
CRIWG'07 Proceedings of the 13th international conference on Groupware: design implementation, and use
Computers in Human Behavior
Activity sequence modelling and dynamic clustering for personalized e-learning
User Modeling and User-Adapted Interaction
A mechanism to support context-based adaptation in m-learning
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Rolling: A new technique for the practical teaching in computer science university degree
Education and Information Technologies
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Detecting students' perception style by using games
Computers & Education
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Learning style models constitute a valuable tool for improving individual learning by the use of adaptation techniques based on them. In this paper, we present how the benefit of considering learning styles with adaptation purposes, as part of the user model, can be extended to the context of collaborative learning as a key feature for group formation. We explore the effects that the combination of students with different learning styles in specific groups may have in the final results of the tasks accomplished by them collaboratively. With this aim, a case study with 166 students of computer science has been carried out, from which conclusions are drawn. We also describe how an existing web-based system can take advantage of learning style information in order to form more productive groups. Our ongoing work concerning the automatic extraction of grouping rules starting from data about previous interactions within the system is also outlined. Finally, we present our challenges, related to the continuous improvement of collaboration by the use and dynamic modification of automatic grouping rules.