Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA

• Yoshitatsu, Matsuda

• Takayuki, Sekiya

• Kazunori, Yamaguchi

抄録

The design of appropriate curricula is one of the most important issues in higher educational institutions, and there are many features to be considered. In this paper, the two key features (“locality bias” and “combination of two simple factors”) were discovered by investigating the actual computer science (CS) curricula of the top-ranked universities on the basis of Computer Science Curricula 2013 (CS2013), where the CS topics are classified into the 18 Knowledge Areas (KAs). We applied a machine learning method named simplified, supervised latent Dirichlet allocation (ssLDA) to the actual syllabi of the CS departments of the 47 top-ranked universities. ssLDA estimates the relative weights of the KAs of CS2013 in each syllabus. Then, each CS department was characterized as the averaged weights of the KAs over its included syllabi. We applied the three well-known data analysis methods (hierarchical cluster analysis, principle component analysis, and non-negative matrix factorization) to the averaged weights of each department and found the above two key features quantitatively and objectively.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.26(2018) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.26.497------------------------------

The design of appropriate curricula is one of the most important issues in higher educational institutions, and there are many features to be considered. In this paper, the two key features (“locality bias” and “combination of two simple factors”) were discovered by investigating the actual computer science (CS) curricula of the top-ranked universities on the basis of Computer Science Curricula 2013 (CS2013), where the CS topics are classified into the 18 Knowledge Areas (KAs). We applied a machine learning method named simplified, supervised latent Dirichlet allocation (ssLDA) to the actual syllabi of the CS departments of the 47 top-ranked universities. ssLDA estimates the relative weights of the KAs of CS2013 in each syllabus. Then, each CS department was characterized as the averaged weights of the KAs over its included syllabi. We applied the three well-known data analysis methods (hierarchical cluster analysis, principle component analysis, and non-negative matrix factorization) to the averaged weights of each department and found the above two key features quantitatively and objectively.------------------------------This is a preprint of an article intended for publication Journal ofInformation Processing(JIP). This preprint should not be cited. Thisarticle should be cited as: Journal of Information Processing Vol.26(2018) (online)DOI　http://dx.doi.org/10.2197/ipsjjip.26.497------------------------------

収録刊行物

• 情報処理学会論文誌

  情報処理学会論文誌 59 (6), 2018-06-15

キーワード

• [一般論文] syllabus, curriculum, curriculum analysis, CS2013, supervised LDA

詳細情報

• CRID: 1050845762839786112
• NII論文ID: 170000149560
• NII書誌ID: AN00116647
• ISSN: 18827764
• Web Site: http://id.nii.ac.jp/1001/00189916/
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles