Building a Recommender Agent for e-Learning Systems
ICCE '02 Proceedings of the International Conference on Computers in Education
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Ontology-Based Personalised and Context-Aware Recommendations of News Items
WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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
Building group recommendations in e-learning systems
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
A novel resource recommendation system based on connecting to similar e-learners
ICWL'05 Proceedings of the 4th international conference on Advances in Web-Based Learning
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Building group recommendations for students enables to suggest colleagues of similar features, with whom they can learn together by using the same teaching materials. Recommendations should depend on the context of use of an e-learning environment. In the paper, it is considered building context-aware recommendations, which aims at indicating suitable learning resources. It is assumed that learners are modeled by attributes of nominal values. It is proposed to use the method based on the Bayes formula. The performance of the technique is validated on the basis of data of students, who are described by cognitive traits such as dominant learning style dimensions. Experiments are done for real data of different groups of similar students as well as of individual learners.