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International Conference on Neural Information Processing

ICONIP 2017: Neural Information Processing pp 186–195Cite as

Discovery of Interconnection Among Knowledge Areas of Standard Computer Science Curricula by a Data Science Approach

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

Abstract

Computer Science Curricula 2013 (CS2013) is a widely-used standard curricula of computer science, which has been developed jointly by the ACM and the IEEE Computer Society. CS2013 consists of 18 Knowledge Areas (KAs) such as Programming Languages and Software Engineering. Though it is obvious that there are strong interconnections among the KAs, it was hard to investigate the interconnections objectively and quantitatively. In this paper, the interconnections among the KAs of CS2013 are investigated by a data science approach. For this purpose, a collection of actual syllabi from the world’s top-ranked universities was constructed. Then, every actual syllabus is projected to the KA space by a probabilistic model-based method named simplified, supervised Latent Dirichlet Allocation (denoted by ssLDA). Consequently, the following interesting properties of the interconnections among the KAs were discovered: (1) There are the high interconnections among the KAs in each syllabi; (2) A plausible hierarchical structure of the KAs is found by utilizing the interconnections; (3) The structure shows that the KAs are classified into the three principal independent factors (HUMAN, THEORY, and IMPLEMENTATION). The factor of IMPLEMENTATION can be divided into PROGRAMMING and SYSTEM. The factor of SYSTEM can be divided further into DEVICES and NETWORK.

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Notes

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Authors and Affiliations

  1. Department of General Systems Studies, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo, 153-8902, Japan

    Yoshitatsu Matsuda & Kazunori Yamaguchi

  2. Information Technology Center, The University of Tokyo, 3-8-1, Komaba, Meguro-ku, Tokyo, 153-8902, Japan

    Takayuki Sekiya

Authors
  1. Yoshitatsu Matsuda
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  2. Takayuki Sekiya
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  3. Kazunori Yamaguchi
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Corresponding author

Correspondence to Yoshitatsu Matsuda .

Editor information

Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Matsuda, Y., Sekiya, T., Yamaguchi, K. (2017). Discovery of Interconnection Among Knowledge Areas of Standard Computer Science Curricula by a Data Science Approach. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_19

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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