IEICE TRANSACTIONS on Information
Learning State Recognition in Self-Paced E-Learning
Summary :
Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.
- Publication
- IEICE TRANSACTIONS on Information Vol.E100-D No.2 pp.340-349
- Publication Date
- 2017/02/01
- Publicized
- 2016/11/21
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2016EDP7144
- Type of Manuscript
- PAPER
- Category
- Educational Technology
Authors
Siyang YU
Kyoto University
Kazuaki KONDO
Kyoto University
Yuichi NAKAMURA
Kyoto University
Takayuki NAKAJIMA
Kyoto University
Masatake DANTSUJI
Kyoto University
Keyword
Latest Issue
Copyrights notice of machine-translated contents
The copyright of the original papers published on this site belongs to IEICE. Unauthorized use of the original or translated papers is prohibited. See IEICE Provisions on Copyright for details.
Cite this
Copy
Siyang YU, Kazuaki KONDO, Yuichi NAKAMURA, Takayuki NAKAJIMA, Masatake DANTSUJI, "Learning State Recognition in Self-Paced E-Learning" in IEICE TRANSACTIONS on Information,
vol. E100-D, no. 2, pp. 340-349, February 2017, doi: 10.1587/transinf.2016EDP7144.
Abstract: Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2016EDP7144/_p
Copy
@ARTICLE{e100-d_2_340,
author={Siyang YU, Kazuaki KONDO, Yuichi NAKAMURA, Takayuki NAKAJIMA, Masatake DANTSUJI, },
journal={IEICE TRANSACTIONS on Information},
title={Learning State Recognition in Self-Paced E-Learning},
year={2017},
volume={E100-D},
number={2},
pages={340-349},
abstract={Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.},
keywords={},
doi={10.1587/transinf.2016EDP7144},
ISSN={1745-1361},
month={February},}
Copy
TY - JOUR
TI - Learning State Recognition in Self-Paced E-Learning
T2 - IEICE TRANSACTIONS on Information
SP - 340
EP - 349
AU - Siyang YU
AU - Kazuaki KONDO
AU - Yuichi NAKAMURA
AU - Takayuki NAKAJIMA
AU - Masatake DANTSUJI
PY - 2017
DO - 10.1587/transinf.2016EDP7144
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E100-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2017
AB - Self-paced e-learning provides much more freedom in time and locale than traditional education as well as diversity of learning contents and learning media and tools. However, its limitations must not be ignored. Lack of information on learners' states is a serious issue that can lead to severe problems, such as low learning efficiency, motivation loss, and even dropping out of e-learning. We have designed a novel e-learning support system that can visually observe learners' non-verbal behaviors and estimate their learning states and that can be easily integrated into practical e-learning environments. Three pairs of internal states closely related to learning performance, concentration-distraction, difficulty-ease, and interest-boredom, were selected as targets of recognition. In addition, we investigated the practical problem of estimating the learning states of a new learner whose characteristics are not known in advance. Experimental results show the potential of our system.
ER -
English
