TY - GEN
T1 - EEG cognition detection to support aptitude-treatment interaction in E-learning platforms
AU - Mwambe, Othmar Othmar
AU - Kamioka, Eiji
N1 - Funding Information:
Acknowledgment Last but not the least we would like to acknowledge financial support from Shibaura Institute of Technology - SIT and cognitive neuroscience consultation from Prof. Horie Ryota of Shibaura Institute of Technology.
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2019 Elsevier B.V., All rights reserved.
PY - 2018/3
Y1 - 2018/3
N2 - E-learning platforms have emerged and played a crucial role in knowledge sharing and dissemination of information at large. However, an optimal knowledge acquisition in e-learning platforms is still a challenge due to poor interactive learning environment. To address that challenge, in this study a correlation between visual spatial attention, learners' motivation states and long-term memory during learning process has been investigated through learners' cognition detection based on their metacognitive experiences by using electroencephalogram (EEG). The obtained results show strong correlation between visual spatial attention, motivation states and long-term memory. Based on the obtained results, this paper proposes brain-computer interface based approach to assist adaptation of learners' motivation states in e-learning platforms. The study paves a way for the aptitude-treatment interaction monitoring and involvement of deaf individuals in e-learning platforms.
AB - E-learning platforms have emerged and played a crucial role in knowledge sharing and dissemination of information at large. However, an optimal knowledge acquisition in e-learning platforms is still a challenge due to poor interactive learning environment. To address that challenge, in this study a correlation between visual spatial attention, learners' motivation states and long-term memory during learning process has been investigated through learners' cognition detection based on their metacognitive experiences by using electroencephalogram (EEG). The obtained results show strong correlation between visual spatial attention, motivation states and long-term memory. Based on the obtained results, this paper proposes brain-computer interface based approach to assist adaptation of learners' motivation states in e-learning platforms. The study paves a way for the aptitude-treatment interaction monitoring and involvement of deaf individuals in e-learning platforms.
KW - Aptitude-treatment interaction
KW - Brain computer interfaces-BCI
KW - Cognition
KW - E-learning
KW - Long-term memory
KW - Metacognition
KW - Motivation states
KW - Visual spatial attention
KW - Working Memory
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U2 - 10.1109/SEATUC.2018.8788854
DO - 10.1109/SEATUC.2018.8788854
M3 - Conference contribution
AN - SCOPUS:85071527763
T3 - Proceedings - 12th SEATUC Symposium, SEATUC 2018
BT - Proceedings - 12th SEATUC Symposium, SEATUC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th South East Asian Technical University Consortium Sysmposium, SEATUC 2018
Y2 - 12 March 2018 through 13 March 2018
ER -