Analysis of ubiquitous-learning logs using spatio-temporal data mining

Kousuke Mouri, Hiroaki Ogata, Noriko Uosaki

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

4 Citations (Scopus)

Abstract

This paper proposes an approach of the spatio-temporal data mining in order to predict next learning steps (next ubiquitous learning logs to be learned) in accordance with their situations or context from past learners' experiences in their daily lives accumulated in the ubiquitous learning system called SCROLL (System for Capturing and Reminding of Learning Log). Ubiquitous learning log (ULL) is defined as a digital record of what learners have learned in their daily life using ubiquitous technologies. It allows learners to log their learning experiences with photos, audios, videos, location, RFID tag and sensor data, and to share and reuse ULL with others. This paper describes some data mining methods using the association analysis in order to detect effective and efficient learning logs for learner from relationships among ubiquitous learning logs collected by a number of the research studies for a long period of the SCROLL project (2011~2014).

Original languageEnglish
Title of host publicationProceedings - IEEE 15th International Conference on Advanced Learning Technologies
Subtitle of host publicationAdvanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015
EditorsNian-Shing Chen, Tzu-Chien Liu, Kinshuk, Ronghuai Huang, Gwo-Jen Hwang, Demetrios G. Sampson, Chin-Chung Tsai
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages96-98
Number of pages3
ISBN (Electronic)9781467373333
DOIs
Publication statusPublished - Sep 14 2015
Event15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015 - Hualien, Taiwan, Province of China
Duration: Jul 6 2015Jul 9 2015

Other

Other15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015
CountryTaiwan, Province of China
CityHualien
Period7/6/157/9/15

Fingerprint

Data Mining
Data mining
Learning
Radio frequency identification (RFID)
learning
Learning systems
Sensors
Radio Frequency Identification Device
experience
video
Technology

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Experimental and Cognitive Psychology
  • Computer Networks and Communications
  • Human-Computer Interaction
  • Education

Cite this

Mouri, K., Ogata, H., & Uosaki, N. (2015). Analysis of ubiquitous-learning logs using spatio-temporal data mining. In N-S. Chen, T-C. Liu, Kinshuk, R. Huang, G-J. Hwang, D. G. Sampson, & C-C. Tsai (Eds.), Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015 (pp. 96-98). [7265274] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICALT.2015.66

Analysis of ubiquitous-learning logs using spatio-temporal data mining. / Mouri, Kousuke; Ogata, Hiroaki; Uosaki, Noriko.

Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015. ed. / Nian-Shing Chen; Tzu-Chien Liu; Kinshuk; Ronghuai Huang; Gwo-Jen Hwang; Demetrios G. Sampson; Chin-Chung Tsai. Institute of Electrical and Electronics Engineers Inc., 2015. p. 96-98 7265274.

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

Mouri, K, Ogata, H & Uosaki, N 2015, Analysis of ubiquitous-learning logs using spatio-temporal data mining. in N-S Chen, T-C Liu, Kinshuk, R Huang, G-J Hwang, DG Sampson & C-C Tsai (eds), Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015., 7265274, Institute of Electrical and Electronics Engineers Inc., pp. 96-98, 15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015, Hualien, Taiwan, Province of China, 7/6/15. https://doi.org/10.1109/ICALT.2015.66
Mouri K, Ogata H, Uosaki N. Analysis of ubiquitous-learning logs using spatio-temporal data mining. In Chen N-S, Liu T-C, Kinshuk, Huang R, Hwang G-J, Sampson DG, Tsai C-C, editors, Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015. Institute of Electrical and Electronics Engineers Inc. 2015. p. 96-98. 7265274 https://doi.org/10.1109/ICALT.2015.66
Mouri, Kousuke ; Ogata, Hiroaki ; Uosaki, Noriko. / Analysis of ubiquitous-learning logs using spatio-temporal data mining. Proceedings - IEEE 15th International Conference on Advanced Learning Technologies: Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015. editor / Nian-Shing Chen ; Tzu-Chien Liu ; Kinshuk ; Ronghuai Huang ; Gwo-Jen Hwang ; Demetrios G. Sampson ; Chin-Chung Tsai. Institute of Electrical and Electronics Engineers Inc., 2015. pp. 96-98
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