 Artificial intelligence-based prediction of student's attention using EEG signals in online learningSwadha Gupta, Parteek Kumar and Rajkumar Tekchandani International Journal of Services, Economics and Management, 2025, vol. 16, issue 4/5, 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. Keywords: EEG waves; brain-computer interaction; machine learning; attention; neurosky. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:injsem:v:16:y:2025:i:4/5:p:342-365 Access Statistics for this article More articles in International Journal of Services, Economics and Management from Inderscience Enterprises Ltd |
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