TY - GEN
T1 - Visualization and prediction of learning activities by using discrete graphs
AU - Okubo, Fumiya
AU - Shimada, Atsushi
AU - Yin, Chengjiu
AU - Ogata, Hiroaki
N1 - Funding Information:
The research results have been achieved by “Research and Development on Fundamental and Utilization Technologies for Social Big Data”, the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan. The work of F. Okubo was in part supported by Kyushu University Interdisciplinary Programs in Education and Projects in Research Development No.27115.
PY - 2015
Y1 - 2015
N2 - This paper presents a method for visualizing students' learning logs using discrete graphs. These logs contain the following four items: attendance, time spent browsing slides, submission of a report and the quiz score for each lesson. The data were collected using learning management systems and the e-text systems. By using these data, we construct graphs for each grade of which the nodes represent all combinations of achievements and failures for the four items. The graphs enable us to observe the features of students' learning activities for each obtained grade. The order in which the above four items are presented changes the visual features of the graph. Moreover, the construction of a graph from the data of the same class held previously enables us to inform students of the learning activities they should avoid. Finally, future research plans regarding this method are presented.
AB - This paper presents a method for visualizing students' learning logs using discrete graphs. These logs contain the following four items: attendance, time spent browsing slides, submission of a report and the quiz score for each lesson. The data were collected using learning management systems and the e-text systems. By using these data, we construct graphs for each grade of which the nodes represent all combinations of achievements and failures for the four items. The graphs enable us to observe the features of students' learning activities for each obtained grade. The order in which the above four items are presented changes the visual features of the graph. Moreover, the construction of a graph from the data of the same class held previously enables us to inform students of the learning activities they should avoid. Finally, future research plans regarding this method are presented.
UR - https://www.scopus.com/pages/publications/85030125022
UR - https://www.scopus.com/pages/publications/85030125022#tab=citedBy
M3 - Conference contribution
AN - SCOPUS:85030125022
T3 - Proceedings of the 23rd International Conference on Computers in Education, ICCE 2015
SP - 739
EP - 744
BT - Proceedings of the 23rd International Conference on Computers in Education, ICCE 2015
A2 - Lan, Yu-Ju
A2 - Shyu, Hsin-Yih
A2 - Yin, Chengjiu
A2 - Ogata, Hiroaki
A2 - Chen, Wenli
A2 - Jan, Ming-Fong
A2 - Murthy, Sahana
A2 - Wu, Ying-Tien
A2 - Kong, Siu Cheung
A2 - Gu, Xiaoqing
A2 - Kim, Beaumie
A2 - Miao, Yongwu
A2 - Srisawasdi, Niwat
A2 - Wang, Yuping
A2 - Lin, Chiu-Pin
A2 - Chu, Carol H.C.
A2 - Laru, Jari
A2 - Nussbaum, Miguel
A2 - Rodrigo, Ma. Mercedes T.
A2 - Shih, Ju-Ling
A2 - Weerasinghe, Amali
A2 - Chen, Weiqin
A2 - Qiu, Feiyue
A2 - Dimitrova, Vania
A2 - Hsu, Ching-Kun
A2 - Wong, Lung-Hsiang
A2 - Chang, Maiga
A2 - Hoel, Tore
A2 - Li, Yen-Hui Audrey
A2 - Mason, Jon
A2 - Sasaki, Hitoshi
A2 - Zhang, Li
PB - Asia-Pacific Society for Computers in Education
T2 - 23rd International Conference on Computers in Education, ICCE 2015
Y2 - 30 November 2015 through 4 December 2015
ER -