Detecting Learning Styles in Learning Management Systems Using Data Mining

• Item type: Trans(1)
• 公開日: 2016-06-01
• タイトル: Detecting Learning Styles in Learning Management Systems Using Data Mining
• タイトル / 言語: en
• タイトル: Detecting Learning Styles in Learning Management Systems Using Data Mining
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [論文] learning management systems, learning styles, moodle, data mining
• 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: Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者所属: Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者所属: Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者所属(英): en
• 著者所属(英): Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者所属(英): en
• 著者所属(英): Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者所属(英): en
• 著者所属(英): Interdisciplinary Graduate School of Science and Engineering, Shimane University
• 著者名: Madura, Prabhani,Pitigala Liyanage; K.S., Lasith Gunawardena; Masahito, Hirakawa
• 著者名(英): Madura, Prabhani Pitigala Liyanage; K.S., Lasith Gunawardena; Masahito, Hirakawa
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: The use of data mining in the education sector has increased in the recent past. One reason for this is the wide use of learning management systems (LMS), which store data related to learning activities. The goal of this research is to predict individual learning styles using the Moodle LMS by analyzing log data using a data mining technique. We use the Waikato environment for knowledge analysis (Weka), as the data mining tool and compare the differences in the performance of several data mining techniques using course log data. Our experimental results show that the J48 decision tree classification algorithm works best with our dataset. We also propose a group learning map that visualizes the learning styles in a class, which can help instructors and learners achieve learning outcomes more effectively.; ------------------------------; 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.Vol.24(2016) No.4(online); ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: The use of data mining in the education sector has increased in the recent past. One reason for this is the wide use of learning management systems (LMS), which store data related to learning activities. The goal of this research is to predict individual learning styles using the Moodle LMS by analyzing log data using a data mining technique. We use the Waikato environment for knowledge analysis (Weka), as the data mining tool and compare the differences in the performance of several data mining techniques using course log data. Our experimental results show that the J48 decision tree classification algorithm works best with our dataset. We also propose a group learning map that visualizes the learning styles in a class, which can help instructors and learners achieve learning outcomes more effectively.; ------------------------------; 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.Vol.24(2016) No.4(online); ------------------------------
• 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 2, 号 1, 発行日 2016-06-01
• 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