Advancing Real-Time Remote Learning: A Novel Paradigm for Cognitive Enhancement Using EEG and Eye-Tracking Analytics

Abstract

This study explores the convergence of biometric analytics and machine learning in online education, where the level of student participation directly impacts academic achievement. In this study, various machine learning models were employed to identify cognitive states using eye-tracking and electroencephalogram data, which can provide quantitative indicators of cognitive activity. A comparative analysis of convolutional neural network (CNN), logistic regression, decision tree, random forest, gradient boosting, support vector machine, and K-nearest neighbors (KNN) models was conducted. The receiver operating characteristic curve was used as a benchmark to assess the effectiveness of the models in interpreting the subtle patterns in biometric feedback. The results show that the CNN model exhibited outstanding performance, achieving an area under the curve value of 0.98 in interpreting intricate data that reflect the levels of student involvement. In addition, the gradient boosting and KNN models exhibited noteworthy accuracy, and the logistic regression and random forest models realized a crucial equilibrium by providing a high level of interpretability and strong performance. This is particularly important for educational applications where the model’s rationale must be transparent. The findings of this study support the idea that machine learning can considerably improve remote learning platforms, leading to more customized and interactive educational experiences. However, the findings also acknowledge relevant ethical concerns and the need to understand these advanced technologies comprehensively.

Keywords

• Electroencephalogram
• eye-tracking
• machine learning
• remote learning