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arXiv · 2502.15107

Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework

Zewen Zhuo·Mohamad Najafi·Hazem Zein·Amine Nait-Ali
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Abstract

This study introduces a specialized pipeline designed to classify the concentration state of an individual student during online learning sessions by training a custom-tailored machine learning model. Detailed protocols for acquiring and preprocessing EEG data are outlined, along with the extraction of fifty statistical features from five EEG signal bands: alpha, beta, theta, delta, and gamma. Following feature extraction, a thorough feature selection process was conducted to optimize the data inputs for a personalized analysis. The study also explores the benefits of hyperparameter fine-tuning to enhance the classification accuracy of the student's concentration state. EEG signals were captured from the student using a Muse headband (Gen 2), equipped with five electrodes (TP9, AF7, AF8, TP10, and a reference electrode NZ), during engagement with educational content on computer-based e-learning platforms. Employing a random forest model customized to the student's data, we achieved remarkable classification performance, with test accuracies of 97.6% in the computer-based learning setting and 98% in the virtual reality setting. These results underscore the effectiveness of our approach in delivering personalized insights into student concentration during online educational activities.

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Zewen Zhuo, Mohamad Najafi, Hazem Zein, Amine Nait-Ali. 2025-02-21. Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework. https://arxiv.org/abs/2502.15107

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