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MCRS: A course recommendation system for MOOCs

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Abstract

With the popularization development of MOOC platform, the number of online courses grows rapidly. Efficient and appropriate course recommendation can improve learning efficiency. Traditional recommendation system is applied to the closed educational environment in which the quantity of courses and users is relatively stable. Recommendation model and algorithm cannot directly be applied to MOOC platform efficiently. With the light of the characteristics of MOOC platform, MCRS proposed in this paper has made great improvement in the course recommendation model and recommendation algorithm. MCRS is based on distributed computation framework. The basic algorithm of MCRS is distributed association rules mining algorithm, which based on the improvement of Apriori algorithm. In addition, it is useful to mine the hidden courses rules in course enrollment data. Firstly, the data is pre-processed into a standard form by Hadoop. It aims to improve the efficiency of the basic algorithm. Then it mines association rules of the standard data by Spark. Consequently, course recommendation information is transferred into MySQL through Sqoop, which makes timely feedback and improves user’s courses retrieval efficiency. Finally, to validate the efficiency of MCRS, a series of experiments are carried out on Hadoop and Spark, and the results shows that MCRS is more efficient than traditional Apriori algorithm and Apriori algorithm based on Hadoop, and the MCRS is suitable for current MOOC platform.

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Acknowledgements

This study was funded by the National Programs for Science and Technology Development (grant number 2015BAK07B03), the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), and specific funding for education science research by self-determined research funds of CCNU from the colleges’ basic research and operation of MOE ((grant number CCNU17QN0004)).

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Authors and Affiliations

  1. National Engineering Research Center for E-Learning, Central China Normal University(CCNU), Room 419,Science Hall, 152 Luoyu Road, Wuhan, 430072, People’s Republic of China

    Hao Zhang, Tao Huang & SanYa Liu

  2. Department of Computer science, University College London, London, UK

    Zhihan Lv

  3. Nanjing University of Information Science & Technology, Jiang su, Nanjing, China

    Zhili Zhou

Authors
  1. Hao Zhang
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  2. Tao Huang
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  3. Zhihan Lv
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  4. SanYa Liu
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  5. Zhili Zhou
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Correspondence to Zhihan Lv.

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Zhang, H., Huang, T., Lv, Z. et al. MCRS: A course recommendation system for MOOCs. Multimed Tools Appl 77, 7051–7069 (2018). https://doi.org/10.1007/s11042-017-4620-2

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  • DOI: https://doi.org/10.1007/s11042-017-4620-2

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