TY - GEN
T1 - Connecting dots for ubiquitous learning analytics
AU - Ogata, Hiroaki
AU - Mouri, Kousuke
N1 - Funding Information:
The part of this research work was supported by the Grant-in-Aid for Scientific Research No.25282059, No.26560122, No.25540091 and No.26350319 from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) in Japan. The research results have been also partly supported by “Research and Development on Fundamental and Utilization Technologies for Social Big Data” (178A03), the Commissioned Research of National Institute of Information and Communications Technology (NICT), Japan.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - A Ubiquitous Learning Log (ULL) is defined as a digital record of what a learner has learned in daily life using ubiquitous computing technologies. It allows learners to log their learning experiences with photos, audios, videos, locations, RFID tag and sensor data, and to share and reuse ULL with others. The number of ULLs will keep increasing as the learners keep learning. The sheer volume of ULLs will be accumulated in the ubiquitous learning system called SCROLL. It creates a necessity to analyze the ubiquitous learning logs to provide learners with appropriate learning logs in accordance with their learning abilities, context, time and location. However, researchers on analysis and visualization on ubiquitous learning is very few, and there are not yet previous works that visualize relationships among learning logs on spatial and temporal dimensions. Therefore, this paper introduces the overview of SCROLL, and then describes an innovative visualization system which integrates network visualization technologies and time-map in order to visualize the ubiquitous learning logs accumulated in the SCROLL.
AB - A Ubiquitous Learning Log (ULL) is defined as a digital record of what a learner has learned in daily life using ubiquitous computing technologies. It allows learners to log their learning experiences with photos, audios, videos, locations, RFID tag and sensor data, and to share and reuse ULL with others. The number of ULLs will keep increasing as the learners keep learning. The sheer volume of ULLs will be accumulated in the ubiquitous learning system called SCROLL. It creates a necessity to analyze the ubiquitous learning logs to provide learners with appropriate learning logs in accordance with their learning abilities, context, time and location. However, researchers on analysis and visualization on ubiquitous learning is very few, and there are not yet previous works that visualize relationships among learning logs on spatial and temporal dimensions. Therefore, this paper introduces the overview of SCROLL, and then describes an innovative visualization system which integrates network visualization technologies and time-map in order to visualize the ubiquitous learning logs accumulated in the SCROLL.
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U2 - 10.1007/978-3-319-20621-9_4
DO - 10.1007/978-3-319-20621-9_4
M3 - Conference contribution
AN - SCOPUS:84951118125
SN - 9783319206202
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 46
EP - 56
BT - Hybrid Learning
A2 - Kwok, Lam-for
A2 - Fong, Joseph
A2 - Cheung, Simon K.S.
A2 - Yang, Harrison
A2 - Fong, Joseph
A2 - Kwan, Reggie
A2 - Yang, Harrison
PB - Springer Verlag
T2 - 8th International Conference on Hybrid Learning, ICHL 2015
Y2 - 27 July 2015 through 29 July 2015
ER -