TY - GEN
T1 - Inactive behavior analytics in on-site lectures
AU - Minematsu, Tsubasa
AU - Saguey, Manon
AU - Shimada, Atsushi
AU - Taniguchi, Rin Ichiro
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by JST AIP Grant JPMJCR19U1, and JSPS KAKENHI Grand JP18H04125, Japan
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/8
Y1 - 2020/12/8
N2 - Detection of at-risk students is a fundamental issue in enhancing learning supports, and has been proposed based on students' learning activity in learning analytics. However, it is not clear which activity we should focus on to detect at-risk students such as low performance students. In this study, we proposed a clustering-based method for at-risk student detection based on three main clusters of students: inactive, passive, active students. Our method focused on reading behaviors and action behaviors in an e-book system. In addition, we consider which period of learning activities is effective for detecting at-risk students. The learning logs of 289 students of Cyber-Security course were collected for our analysis. In our comparison at different moment during the lecture, we found that the cluster of inactive students detected after 35 minutes of lecture got significant lower grades than other students, when the lecture was not too short nor too easy.
AB - Detection of at-risk students is a fundamental issue in enhancing learning supports, and has been proposed based on students' learning activity in learning analytics. However, it is not clear which activity we should focus on to detect at-risk students such as low performance students. In this study, we proposed a clustering-based method for at-risk student detection based on three main clusters of students: inactive, passive, active students. Our method focused on reading behaviors and action behaviors in an e-book system. In addition, we consider which period of learning activities is effective for detecting at-risk students. The learning logs of 289 students of Cyber-Security course were collected for our analysis. In our comparison at different moment during the lecture, we found that the cluster of inactive students detected after 35 minutes of lecture got significant lower grades than other students, when the lecture was not too short nor too easy.
UR - http://www.scopus.com/inward/record.url?scp=85102971043&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85102971043&partnerID=8YFLogxK
U2 - 10.1109/TALE48869.2020.9368453
DO - 10.1109/TALE48869.2020.9368453
M3 - Conference contribution
AN - SCOPUS:85102971043
T3 - Proceedings of 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2020
SP - 708
EP - 713
BT - Proceedings of 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2020
A2 - Mitsuhara, Hiroyuki
A2 - Goda, Yoshiko
A2 - Ohashi, Yutato
A2 - Rodrigo, Ma. Mercedes T.
A2 - Shen, Jun
A2 - Venkatarayalu, Neelakantam
A2 - Wong, Gary
A2 - Yamada, Masanori
A2 - Chi-Un Lei, Leon
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Teaching, Assessment, and Learning for Engineering, TALE 2020
Y2 - 8 December 2020 through 11 December 2020
ER -