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 language | English |
|---|---|
| Title of host publication | Proceedings - IEEE 15th International Conference on Advanced Learning Technologies |
| Subtitle of host publication | Advanced Technologies for Supporting Open Access to Formal and Informal Learning, ICALT 2015 |
| Editors | Nian-Shing Chen, Tzu-Chien Liu, Kinshuk, Ronghuai Huang, Gwo-Jen Hwang, Demetrios G. Sampson, Chin-Chung Tsai |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 96-98 |
| Number of pages | 3 |
| ISBN (Electronic) | 9781467373333 |
| DOIs | |
| Publication status | Published - Sep 14 2015 |
| Event | 15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015 - Hualien, Taiwan, Province of China Duration: Jul 6 2015 → Jul 9 2015 |
Other
| Other | 15th IEEE International Conference on Advanced Learning Technologies, ICALT 2015 |
|---|---|
| Country | Taiwan, Province of China |
| City | Hualien |
| Period | 7/6/15 → 7/9/15 |
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All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Experimental and Cognitive Psychology
- Computer Networks and Communications
- Human-Computer Interaction
- Education
Cite this
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 proceeding › Conference contribution
}
TY - GEN
T1 - Analysis of ubiquitous-learning logs using spatio-temporal data mining
AU - Mouri, Kousuke
AU - Ogata, Hiroaki
AU - Uosaki, Noriko
PY - 2015/9/14
Y1 - 2015/9/14
N2 - 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).
AB - 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).
UR - http://www.scopus.com/inward/record.url?scp=84961736545&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84961736545&partnerID=8YFLogxK
U2 - 10.1109/ICALT.2015.66
DO - 10.1109/ICALT.2015.66
M3 - Conference contribution
AN - SCOPUS:84961736545
SP - 96
EP - 98
BT - Proceedings - IEEE 15th International Conference on Advanced Learning Technologies
A2 - Chen, Nian-Shing
A2 - Liu, Tzu-Chien
A2 - Kinshuk, null
A2 - Huang, Ronghuai
A2 - Hwang, Gwo-Jen
A2 - Sampson, Demetrios G.
A2 - Tsai, Chin-Chung
PB - Institute of Electrical and Electronics Engineers Inc.
ER -