Intuitive Analysis by Visualizing Context Relevant E-learning Data

• Item type: Trans(1)
• 公開日: 2017-06-14
• タイトル: Intuitive Analysis by Visualizing Context Relevant E-learning Data
• タイトル / 言語: en
• タイトル: Intuitive Analysis by Visualizing Context Relevant E-learning Data
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [ショートペーパー] visualization, visual analysis, dimensional reductions, neural networks, Self-Organizing Maps
• 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: School of Engineering, Chukyo University／Presently with Humanoid Research Institute, Waseda University
• 著者所属: Faculty of Science, Japan Women's University
• 著者所属(英): en
• 著者所属(英): School of Engineering, Chukyo University / Presently with Humanoid Research Institute, Waseda University
• 著者所属(英): en
• 著者所属(英): Faculty of Science, Japan Women's University
• 著者名: Pitoyo, Hartono; Kayo, Ogawa
• 著者名(英): Pitoyo, Hartono; Kayo, Ogawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: In the last few years learning management systems have been widely introduced in many educational institutions with the primary objectives of supporting students with more flexible learning environments and also importantly acquiring learning pattern data from students and extracting meaningful contents from the data to be used to improve the learning quality. However, often due to the complexity and the multidimensionality of the data, the extraction of meaningful information from them is difficult. So far many methods for mining useful information from complex data have been proposed, and one of the most powerful is visualization that allows intuitive understanding on the underlying properties of the data. In this paper, visualization of E-learning data using a newly introduced context-oriented self-organizing map is introduced and compared against some traditional visualization methods.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: In the last few years learning management systems have been widely introduced in many educational institutions with the primary objectives of supporting students with more flexible learning environments and also importantly acquiring learning pattern data from students and extracting meaningful contents from the data to be used to improve the learning quality. However, often due to the complexity and the multidimensionality of the data, the extraction of meaningful information from them is difficult. So far many methods for mining useful information from complex data have been proposed, and one of the most powerful is visualization that allows intuitive understanding on the underlying properties of the data. In this paper, visualization of E-learning data using a newly introduced context-oriented self-organizing map is introduced and compared against some traditional visualization methods.
• 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: AA12697953
• 書誌情報: 情報処理学会論文誌教育とコンピュータ（TCE）; 巻 3, 号 2, p. 20-27, 発行日 2017-06-14
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-4234
• 出版者 / 言語: ja
• 出版者: 情報処理学会