TY - GEN
T1 - In-process Feedback by Detecting Deadlock based on EEG Data in Exercise of Learning by Problem-posing
AU - Yamamoto, Sho
AU - Tobe, Yuto
AU - Tawatsuji, Yoshimasa
AU - Hirashima, Tsukasa
N1 - Publisher Copyright:
© 2021 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings. All rights reserved
PY - 2021/11/22
Y1 - 2021/11/22
N2 - Giving feedback to learning activities is one of the most important issues so as to realize adaptive learning. Feedback for the product of the activity (we call it “after-process feedback”) has previously been implemented in many interactive and adaptive learning environments. However, feedback during the activity (we call it “in-process feedback”) has been hardly implemented. When a learner gets stuck or frustrated during some stage of the process, in-process feedback is much better than after-process feedback. The difficulty in realizing in-process feedback lies in the timing and content of the feedback. To solve this, we developed and implemented affect detection based on EEG data for deciding the timing of the feedback, and knowledge state estimation based on knowledge structure for the content of the feedback. Furthermore, in this study, we realize and evaluate the in-process feedback by detecting deadlocks based on EEG data for learning through problem-posing.
AB - Giving feedback to learning activities is one of the most important issues so as to realize adaptive learning. Feedback for the product of the activity (we call it “after-process feedback”) has previously been implemented in many interactive and adaptive learning environments. However, feedback during the activity (we call it “in-process feedback”) has been hardly implemented. When a learner gets stuck or frustrated during some stage of the process, in-process feedback is much better than after-process feedback. The difficulty in realizing in-process feedback lies in the timing and content of the feedback. To solve this, we developed and implemented affect detection based on EEG data for deciding the timing of the feedback, and knowledge state estimation based on knowledge structure for the content of the feedback. Furthermore, in this study, we realize and evaluate the in-process feedback by detecting deadlocks based on EEG data for learning through problem-posing.
KW - EEG
KW - Problem-posing
KW - in-process feedback
KW - knowledge structure
KW - wheel-spinning problem
UR - http://www.scopus.com/inward/record.url?scp=85126592515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85126592515&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85126592515
T3 - 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
SP - 21
EP - 30
BT - 29th International Conference on Computers in Education Conference, ICCE 2021 - Proceedings
A2 - Rodrigo, Maria Mercedes T.
A2 - Iyer, Sridhar
A2 - Mitrovic, Antonija
A2 - Cheng, Hercy N. H.
A2 - Kohen-Vacs, Dan
A2 - Matuk, Camillia
A2 - Palalas, Agnieszka
A2 - Rajenran, Ramkumar
A2 - Seta, Kazuhisa
A2 - Wang, Jingyun
PB - Asia-Pacific Society for Computers in Education
T2 - 29th International Conference on Computers in Education Conference, ICCE 2021
Y2 - 22 November 2021 through 26 November 2021
ER -