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Adaptive Neuro-learning: A New Education Strategy using EEG-based Passive Brain-Computer Interfaces

Title
Adaptive Neuro-learning: A New Education Strategy using EEG-based Passive Brain-Computer Interfaces
Other Titles
뇌파 기반 수동형 뇌-컴퓨터 인터페이스를 이용한 사용자 맞춤형 뉴로 에듀케이션
Author
채연수
Alternative Author(s)
채연수
Advisor(s)
임창환
Issue Date
2020-02
Publisher
한양대학교
Degree
Master
Abstract
교실에서 이루어지는 기존의 대면 학습과는 달리, e-러닝이나 온라인 학습은 일반적으로 상호적인 학습이 불가능하다는 결정적인 한계를 지니고 있다. 이 문제를 해결하기 위해, EEG 신호를 통해 학습자의 정신 상태(본 연구에서는 주의집중 상태)를 실시간으로 모니터링하고 그 상태에 따라 다양한 학습 내용을 제공하는, 즉, 개인맞춤형 학습을 제공하는 수동형 뇌-컴퓨터 인터페이스(BCI) 기반의 새로운 e-러닝 시스템을 구현하였다. 본 연구에서는, 시스템의 교육 효과를 평가하기 위해 준 실험 설계를 실시했다. 한 대학교에서 비슷한 학습능력을 가진 총 45명의 학생들이 이 연구에 참여하였고 이 시스템을 적용하여 학습을 한 실험군과 이 시스템 없이 학습을 진행한 두 개의 대조군, 총 세 개의 그룹에 무작위로 배정되었다. 각 그룹의 학습효과를 정량적으로 평가한 결과, 실험군에서 훨씬 더 높은 학습 성과를 보여주었으며, 이는 새로운 학습 전략의 실현가능성과 실용성을 보여주었다.|Contrary to the traditional face-to-face learning, e-learning or online learning has a critical limitation that interactive learning is not generally possible. To circumvent this issue, we implemented a novel e-learning system based on passive brain-computer interface (BCI) using electroencephalography (EEG) data, which seamlessly monitors learner’s mental states (attention state in this study) through EEG signals and provides variety of learning contents according to his/her mental states, hence, provides personalized learning. In this study, a quasi-experimental design was conducted to evaluate the educational effects of the system. Totally, 45 university students from a university in south Korea participated in the study and were randomly assigned to three groups; an experimental group with the system and two control groups without the system. We quantitatively evaluated the educational effects of each group and our results exhibited significantly higher learning performance in the experimental group, demonstrating the feasibility and practicality of the new education strategy.; Contrary to the traditional face-to-face learning, e-learning or online learning has a critical limitation that interactive learning is not generally possible. To circumvent this issue, we implemented a novel e-learning system based on passive brain-computer interface (BCI) using electroencephalography (EEG) data, which seamlessly monitors learner’s mental states (attention state in this study) through EEG signals and provides variety of learning contents according to his/her mental states, hence, provides personalized learning. In this study, a quasi-experimental design was conducted to evaluate the educational effects of the system. Totally, 45 university students from a university in south Korea participated in the study and were randomly assigned to three groups
URI
https://repository.hanyang.ac.kr/handle/20.500.11754/123463http://hanyang.dcollection.net/common/orgView/200000436681
Appears in Collections:
GRADUATE SCHOOL[S](대학원) > BIOMEDICAL ENGINEERING(생체공학과) > Theses (Master)
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