IMPROVING ONLINE LEARNER COMPREHENSION VIA EEG SIGNAL ANALYSIS

In the contemporary landscape of online learning, accurately predicting learner comprehension is pivotal for personalized and effective educational experiences. Traditional methods of assessment often lack real-time feedback and rely heavily on subjective evaluations. In this study, we propose a novel approach to enhance online learner comprehension prediction through the analysis of Electroencephalography (EEG) signals. EEG signals offer a direct window into the brain’s cognitive processes, providing valuable insights into learner engagement and comprehension levels. Our research aims to develop a robust predictive model by leveraging machine learning algorithms and advanced signal processing techniques to analyze EEG data collected during online learning sessions. Through the identification of distinctive EEG patterns associated with varying comprehension levels, we seek to train a predictive model capable of accurately classifying learner comprehension in real time. Furthermore, we aim to investigate the effectiveness of incorporating additional contextual features, such as user interactions and environmental factors, to improve prediction accuracy further. To validate the proposed approach, extensive experiments will be conducted using datasets collected from online learning platforms, encompassing a diverse range of learners and educational content. Performance evaluation will be carried out using metrics such as accuracy, precision, recall, and F1-score, with comparisons against existing methods to demonstrate the superiority of the proposed model. The outcomes of this research hold significant implications for the field of online education, offering educators and learning platforms valuable insights into learner comprehension and engagement dynamics. By enabling real-time comprehension prediction, our approach facilitates adaptive learning experiences tailored to individual learner needs, ultimately enhancing the effectiveness and efficiency of online education delivery.