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Utilizing learning process to improve recommender system for group learning support

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Abstract

With the rapid increasing of learning materials and learning objects in e-learning, the need for recommender system has also become more and more imperative. Although, the traditional recommendation system has achieved great success in many domains, it is not suitable to support e-learning recommender system because the approach in e-learning is hybrid and it is obtained mainly by two mechanisms: the learners’ learning processes and the analysis of social interaction. Therefore, this study proposes a flexible recommendation approach to satisfy this demand. The recommendation is designed based on a multidimensional recommendation model. Furthermore, we use Markov Chain Model to divide the group learners into advanced learners and beginner learners by using the learners’ learning activities and learning processes so that we can correctly estimate the rating which also include learners’ social interaction. The experimental result shows that the proposed system can give a more satisfying and qualified recommendation.

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Authors and Affiliations

  1. Graduate School of Information System, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu-shi, 182-8585, Tokyo, Japan

    Xin Wan & Toshio Okamoto

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  1. Xin Wan
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  2. Toshio Okamoto
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Correspondence to Xin Wan.

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Wan, X., Okamoto, T. Utilizing learning process to improve recommender system for group learning support. Neural Comput & Applic 20, 611–621 (2011). https://doi.org/10.1007/s00521-009-0283-x

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