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Course Relatedness Based on Concept Graph Modeling

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Collaborate Computing: Networking, Applications and Worksharing (CollaborateCom 2016)

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Abstract

Analyzing the relatedness between courses can help students plan their own curricula more efficiently, especially for the learning on MOOC platforms. However, there are few researchers that concentrate on mining the relationship between courses. In this paper, we propose a method to compare relatedness between courses based on representing courses as concept graphs. The concept graph comprises not only the semantic relationship between concepts but also the importance of concepts in the course. Moreover, we take a cluster analysis to find relevant concepts between two courses and take advantage of Similar Concept Groups to compute the degree of course relatedness. We experimented with a collection of English syllabi from Beihang University and experiments show better performance than the state-of-the-art.

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Notes

  1. 1.

    https://code.google.com/p/word2vec.

  2. 2.

    https://github.com/idio/wiki2vec.

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Authors and Affiliations

  1. School of Computer Science and Engineering, Beihang University, Beijing, 100191, China

    Pang Jingwen, Cao Qinghua & Sun Qing

  2. School of Economics and Management, Beihang University, Beijing, 100191, China

    Sun Qing

Authors
  1. Pang Jingwen
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  2. Cao Qinghua
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  3. Sun Qing
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Corresponding author

Correspondence to Sun Qing .

Editor information

Editors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, China

    Shangguang Wang

  2. Beijing University of Posts and Telecommunications, Beijing, China

    Ao Zhou

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© 2017 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Jingwen, P., Qinghua, C., Qing, S. (2017). Course Relatedness Based on Concept Graph Modeling. In: Wang, S., Zhou, A. (eds) Collaborate Computing: Networking, Applications and Worksharing. CollaborateCom 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 201. Springer, Cham. https://doi.org/10.1007/978-3-319-59288-6_9

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  • DOI: https://doi.org/10.1007/978-3-319-59288-6_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59287-9

  • Online ISBN: 978-3-319-59288-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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