Connecting dots for ubiquitous learning analytics

Hiroaki Ogata, Kousuke Mouri

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationHybrid Learning
Subtitle of host publicationInnovation in Educational Practices - 8th International Conference, ICHL 2015, Proceedings
EditorsLam-for Kwok, Joseph Fong, Simon K.S. Cheung, Harrison Yang, Joseph Fong, Reggie Kwan, Harrison Yang
PublisherSpringer Verlag
Number of pages11
ISBN (Print)9783319206202
Publication statusPublished - 2015
Event8th International Conference on Hybrid Learning, ICHL 2015 - Wuhan, China
Duration: Jul 27 2015Jul 29 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th International Conference on Hybrid Learning, ICHL 2015

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)


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