Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA

• Item type: Journal(1)
• 公開日: 2018-06-15
• タイトル: Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA
• タイトル / 言語: en
• タイトル: Curriculum Analysis of Computer Science Departments by Simplified, Supervised LDA
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [一般論文] syllabus, curriculum, curriculum analysis, CS2013, supervised LDA
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_6501
• 資源タイプ: journal article
• 著者所属: Graduate School of Arts and Sciences, The University of Tokyo
• 著者所属: Information Technology Center, The University of Tokyo
• 著者所属: Graduate School of Arts and Sciences, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Arts and Sciences, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Information Technology Center, The University of Tokyo
• 著者所属(英): en
• 著者所属(英): Graduate School of Arts and Sciences, The University of Tokyo
• 著者名: Yoshitatsu, Matsuda; Takayuki, Sekiya; Kazunori, Yamaguchi
• 著者名(英): Yoshitatsu, Matsuda; Takayuki, Sekiya; Kazunori, Yamaguchi
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: 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 of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.497; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: 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 of; Information Processing(JIP). This preprint should not be cited. This; article should be cited as: Journal of Information Processing Vol.26(2018) (online); DOI http://dx.doi.org/10.2197/ipsjjip.26.497; ------------------------------
• 書誌レコードID / 収録物識別子タイプ: NCID
• 書誌レコードID / 収録物識別子: AN00116647
• 書誌情報: 情報処理学会論文誌; 巻 59, 号 6, 発行日 2018-06-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764