Building Group Recommendations in E-Learning Systems

Building groups of students of similar features enables to suggest teaching materials according to their member needs. In the paper, it is proposed the agent-based recommender system, which, for each new learner, suggests the student group of similar profiles and consequently indicates suitable learning resources. It is assumed that learners may be characterized by cognitive styles, usability preferences or historical behavior, represented by nominal values. It is considered to build recommendations by using Naive Bayes algorithm. The performance of the technique is validated on the basis of data of learners described by cognitive traits such as dominant learning style dimensions. Tests are done for real data of different groups of similar students as well as of individual learners.

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