Development of a computer-aided education system inspired by face-to-face learning by incorporating EEG-based neurofeedback into online video lectures
Abstract
Owing to the rapid development of information and communication technologies, online or mobile learning content is widely available on the Internet. Unlike traditional face-to-face learning, online learning exhibits a critical limitation: real-time interactions between learners and teachers are generally not feasible in online learning. To overcome this issue, we implemented an online learning system based on electroencephalography (EEG)-based passive brain-computer interface technology referred to as the “adaptive neuro-learning system (ANLS).” It monitors the current mental states of learners seamlessly using EEG signals. Then, it adaptively provides natural and interactive video feedback rather than simple alarms or pop quizzes following the current mental conditions of a learner. In this article, a total of 60 university students were assigned randomly to one of four groups: two experimental groups, for which either ANLS based on attention state estimation or ANLS based on both attention and comprehension states estimation was tested, and two control groups, the students in which were taught using either the conventional online lecture without feedback or an online course with randomized video feedbacks. Each member of these groups attended a 53 min open courseware video lecture. Then, the educational effects of the proposed system were evaluated quantitatively via a written examination. Our results revealed a significantly higher learning performance for the experimental group (average test score of the experimental groups = 83.83 and that of the control groups = 56.67), demonstrating the feasibility of the proposed education strategy.
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Formative Feedback and Assessment tools together with Real-Time Feedback systems like EEG-Based Neurofeedback provide immediate actionable insights that substantially improve learning performance.
“Formative Feedback and Assessment tools [178], together with Real-Time Feedback systems like EEG-Based Neurofeedback, provide immediate actionable insights that substantially improve learning performance [160].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [160]
Neurofeedback systems monitoring state of mind in real-time improve memory retention and numeric problem-solving.
“Neurofeedback systems, which monitor the state of the mind in real-time, improve the capability of memory retention and numeric problem-solving [160].”
From Leveraging AI in E-Learning: Personalized Learning and Adaptive Assessment through Cognitive Neuropsychology—A Systematic Analysis (2024) · cites this paper as [160]
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- Authors
- Kim, H., Chae, Y., Kim, S., & Im, C.-H.
- Affiliations
- Hanyang University (KR)
- Venue
- IEEE Transactions on Learning Technologies
- Published
- 2023-02-01
- Volume
- 16
- Issue
- 1
- Pages
- 78-91
- ISSN
- 1939-1382 (ISSN-L)
- 2372-0050
- OpenAlex
- W4292979906
- Cited by
- 17
- References
- 53
- Citations by year
- 2026: 6 · 2025: 4 · 2024: 6 · 2022: 1
- FWCI
- 1.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Learning Technologies (publishedVersion)
- Funders
- National Research Foundation of Korea (2019R1A2C2086593)
- Institute of Information and Communications Technology Planning and Evaluation (2020-0-01373)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- EEG-based neurofeedback, online learning, adaptive learning systems, passive brain-computer interface, video feedback, computer-assisted instruction, learning performance
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