Can Sakai Log Data Improve Learning Analytics? Findings from a Preliminary Survey

• Item type: SIG Technical Reports(1)
• 公開日: 2021-03-18
• タイトル: Can Sakai Log Data Improve Learning Analytics? Findings from a Preliminary Survey
• タイトル / 言語: en
• タイトル: Can Sakai Log Data Improve Learning Analytics? Findings from a Preliminary Survey
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: セッション1
• 資源タイプ / 資源タイプ識別子: http://purl.org/coar/resource_type/c_18gh
• 資源タイプ: technical report
• 著者所属: Research Center for Computing and Multimedia Studies, Hosei University
• 著者所属: Graduate School of Science and Engineering, Hosei University
• 著者所属: Graduate School of Science and Engineering, Hosei University
• 著者所属: Graduate School of Science and Engineering, Hosei University
• 著者所属: Research Center for Computing and Multimedia Studies, Hosei University
• 著者所属: Research Center for Computing and Multimedia Studies, Hosei University
• 著者所属(英): en
• 著者所属(英): Research Center for Computing and Multimedia Studies, Hosei University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Hosei University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Hosei University
• 著者所属(英): en
• 著者所属(英): Graduate School of Science and Engineering, Hosei University
• 著者所属(英): en
• 著者所属(英): Research Center for Computing and Multimedia Studies, Hosei University
• 著者所属(英): en
• 著者所属(英): Research Center for Computing and Multimedia Studies, Hosei University
• 著者名: Mohammad, Nehal Hasnine; Ho, Tan Nguyen; Huyen, T. T. Bui; Thuy, Thi Thu Tran; Hisashi, Hatakeyama; Hiroshi, Ueda
• 著者名(英): Mohammad, Nehal Hasnine; Ho, Tan Nguyen; Huyen, T. T. Bui; Thuy, Thi Thu Tran; Hisashi, Hatakeyama; Hiroshi, Ueda
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: As learning analytics is getting maturity, higher educational institutions worldwide are becoming more interested in practicing learning analytics. Over the last decade, learning management systems (LMSs) such as Moodle, Canvas, Blackboard, Sakai were the primary sources for interaction data. This paper reviewed LAK, EDM, and ACM proceedings and associate journals to shed light on Sakai LMS's potential as it offers flexible tools for teaching, learning, and dynamic collaboration. In comparison with other LMSs, Sakai was less explored by the learning analytics community. This paper also discusses Sakai-generated data's potential to cope with future research trends in learning analytics where AI would be used to make a broader impact on education. The findings lead to- with the advances of AI in education, Sakai data could leverage in designing new methods and tools for decision making, augment learners' productivity, regulate self-learning and knowledge extraction. Sakai and sophisticated learning systems could show promises in conventional learning research, including at-risk detection, drop-out prediction, student modeling, behavior analysis, and the human learning process.
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: As learning analytics is getting maturity, higher educational institutions worldwide are becoming more interested in practicing learning analytics. Over the last decade, learning management systems (LMSs) such as Moodle, Canvas, Blackboard, Sakai were the primary sources for interaction data. This paper reviewed LAK, EDM, and ACM proceedings and associate journals to shed light on Sakai LMS's potential as it offers flexible tools for teaching, learning, and dynamic collaboration. In comparison with other LMSs, Sakai was less explored by the learning analytics community. This paper also discusses Sakai-generated data's potential to cope with future research trends in learning analytics where AI would be used to make a broader impact on education. The findings lead to- with the advances of AI in education, Sakai data could leverage in designing new methods and tools for decision making, augment learners' productivity, regulate self-learning and knowledge extraction. Sakai and sophisticated learning systems could show promises in conventional learning research, including at-risk detection, drop-out prediction, student modeling, behavior analysis, and the human learning process.
• 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: AA12496725
• 書誌情報: 研究報告教育学習支援情報システム（CLE）; 巻 2021-CLE-33, 号 3, p. 1-4, 発行日 2021-03-18
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 2188-8620
• Notice: SIG Technical Reports are nonrefereed and hence may later appear in any journals, conferences, symposia, etc.
• 出版者 / 言語: ja
• 出版者: 情報処理学会