Article: Artificial intelligence-based prediction of student's attention using EEG signals in online learning Journal: International Journal of Services, Economics and Management (IJSEM) 2025 Vol.16 No.4/5 pp.342 - 365 Abstract: It is indeed true that the COVID-19 pandemic has disrupted traditional teaching methods, and e-learning has become a prominent mode of learning. However, ensuring student engagement and motivation during e-learning sessions is a challenging task for educators. The proposed EEG-based learning framework can potentially provide a solution to this problem. By analysing the EEG data, the framework can evaluate the student's learning behaviour and predict whether they are attentive or inattentive. The proposed attention evaluation algorithm are innovative and can potentially provide a better understanding of the student's learning behaviour. The SVM algorithm's accuracy of 91.68% is impressive and suggests that the proposed approach has a high potential for accurately predicting a student's learning state. The findings of this research can potentially provide valuable insights for educators to design effective e-learning environments and monitor student's learning behaviour in real-time. Inderscience Publishers - linking academia, business and industry through research

Title: Artificial intelligence-based prediction of student's attention using EEG signals in online learning

Authors: Swadha Gupta; Parteek Kumar; Rajkumar Tekchandani

Addresses: Department of Computer Science and Engineering Technology, Thapar Institute of Engineering and Technology, Bhadson Rd., Adarsh Nagar, Prem Nagar, Patiala, Punjab 147004, India ' Department of Computer Science and Engineering Technology, Thapar Institute of Engineering and Technology, Bhadson Rd., Adarsh Nagar, Prem Nagar, Patiala, Punjab 147004, India ' Department of Computer Science and Engineering Technology, Thapar Institute of Engineering and Technology, Bhadson Rd., Adarsh Nagar, Prem Nagar, Patiala, Punjab 147004, India

Abstract: It is indeed true that the COVID-19 pandemic has disrupted traditional teaching methods, and e-learning has become a prominent mode of learning. However, ensuring student engagement and motivation during e-learning sessions is a challenging task for educators. The proposed EEG-based learning framework can potentially provide a solution to this problem. By analysing the EEG data, the framework can evaluate the student's learning behaviour and predict whether they are attentive or inattentive. The proposed attention evaluation algorithm are innovative and can potentially provide a better understanding of the student's learning behaviour. The SVM algorithm's accuracy of 91.68% is impressive and suggests that the proposed approach has a high potential for accurately predicting a student's learning state. The findings of this research can potentially provide valuable insights for educators to design effective e-learning environments and monitor student's learning behaviour in real-time.

Keywords: EEG waves; brain-computer interaction; machine learning; attention; neurosky.

DOI: 10.1504/IJSEM.2025.148470

International Journal of Services, Economics and Management, 2025 Vol.16 No.4/5, pp.342 - 365

Received: 11 Apr 2023
Accepted: 13 Jun 2023
Published online: 08 Sep 2025 *

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Title: Artificial intelligence-based prediction of student's attention using EEG signals in online learning

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