@article{oai:ipsj.ixsq.nii.ac.jp:00217591,
 author = {Shunpei, Yamaguchi and Shusuke, Ohtawa and Ritsuko, Oshima and Jun, Oshima and Takuya, Fujihashi and Shunsuke, Saruwatari and Takashi, Watanabe and Shunpei, Yamaguchi and Shusuke, Ohtawa and Ritsuko, Oshima and Jun, Oshima and Takuya, Fujihashi and Shunsuke, Saruwatari and Takashi, Watanabe},
 issue = {3},
 journal = {情報処理学会論文誌},
 month = {Mar},
 note = {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
------------------------------, 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
------------------------------},
 title = {An IoT System with Business Card-Type Sensors for Collaborative Learning Analysis},
 volume = {63},
 year = {2022}
}