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Transactions on Computational Collective Intelligence VII pp 144–163Cite as

Building Group Recommendations in E-Learning Systems

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Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 7270))

Abstract

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

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Authors and Affiliations

  1. Institute of Information Technology, Technical University of Lodz, Wolczanska 215, 90-924, Lodz, Poland

    Danuta Zakrzewska

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  1. Danuta Zakrzewska
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Editors and Affiliations

  1. InstituteDepartment of Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc Thanh Nguyen

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Zakrzewska, D. (2012). Building Group Recommendations in E-Learning Systems. In: Nguyen, N.T. (eds) Transactions on Computational Collective Intelligence VII. Lecture Notes in Computer Science, vol 7270. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32066-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-32066-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32065-1

  • Online ISBN: 978-3-642-32066-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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