An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis

• Item type: Journal(1)
• 公開日: 2022-03-15
• タイトル: An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis
• タイトル / 言語: en
• タイトル: An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis
• 言語: eng
• キーワード / 主題Scheme: Other
• キーワード / 主題: [特集:若手研究者] collaborative learning, human activity recognition, sensor-based learning analysis, time synchronization
• 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 Information Science and Technology, Osaka University
• 著者所属: Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属: Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属: Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属: Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Integrated Science and Technology, Shizuoka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者所属(英): en
• 著者所属(英): Graduate School of Information Science and Technology, Osaka University
• 著者名: Shunpei, Yamaguchi; Shusuke, Ohtawa; Ritsuko, Oshima; Jun, Oshima; Takuya, Fujihashi; Shunsuke, Saruwatari; Takashi, Watanabe
• 著者名(英): Shunpei, Yamaguchi; Shusuke, Ohtawa; Ritsuko, Oshima; Jun, Oshima; Takuya, Fujihashi; Shunsuke, Saruwatari; Takashi, Watanabe
• 論文抄録 / 内容記述タイプ: Other
• 論文抄録 / 内容記述: Collaborative learning practices foster the ability to solve creative problems in collaboration with other learners. The collaboration enables learners to learn new ideas from other learners and enhances the social ability of the learners through interaction with other learners. Although the learning science field now uses qualitative analysis to analyze the effects of the collaborative discourse, qualitative analysis requires much human and time costs to analyze the collaborative discourse with dozens of students. This study proposes Sensor-based Regulation Profiler to reduce the analysis costs. The proposed scheme consists of the business card-type sensors that acquire sensor data from each learner with a precise time synchronization as well as learning analysis methods that analyze the collaborative discourse from the acquired sensor data. Experimental evaluations using the proposed scheme showed that the proposed business card-type sensors realized a time synchronization error of 7.7μs on average across the sensors. In addition, the proposed learning analysis could extract and visualize the collaborative activity of each learner in the collaborative discourse through the social graph extraction, learning phase extraction, speaker identification, and activity estimation by using the sensor data from the proposed business card-type sensors.; ------------------------------; 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.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.238; ------------------------------
• 論文抄録(英) / 内容記述タイプ: Other
• 論文抄録(英) / 内容記述: Collaborative learning practices foster the ability to solve creative problems in collaboration with other learners. The collaboration enables learners to learn new ideas from other learners and enhances the social ability of the learners through interaction with other learners. Although the learning science field now uses qualitative analysis to analyze the effects of the collaborative discourse, qualitative analysis requires much human and time costs to analyze the collaborative discourse with dozens of students. This study proposes Sensor-based Regulation Profiler to reduce the analysis costs. The proposed scheme consists of the business card-type sensors that acquire sensor data from each learner with a precise time synchronization as well as learning analysis methods that analyze the collaborative discourse from the acquired sensor data. Experimental evaluations using the proposed scheme showed that the proposed business card-type sensors realized a time synchronization error of 7.7μs on average across the sensors. In addition, the proposed learning analysis could extract and visualize the collaborative activity of each learner in the collaborative discourse through the social graph extraction, learning phase extraction, speaker identification, and activity estimation by using the sensor data from the proposed business card-type sensors.; ------------------------------; 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.30(2022) (online); DOI http://dx.doi.org/10.2197/ipsjjip.30.238; ------------------------------
• 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 63, 号 3, 発行日 2022-03-15
• ISSN / 収録物識別子タイプ: ISSN
• ISSN / 収録物識別子: 1882-7764