Assessing a Single Student's Concentration on Learning Platforms: A Machine Learning-Enhanced EEG-Based Framework
This work addresses the need for personalized monitoring of student engagement in online education, though it is incremental as it applies existing methods like random forests to a specific domain.
The study tackled the problem of classifying a single student's concentration state during online learning by developing a machine learning pipeline using EEG data, achieving test accuracies of 97.6% in computer-based and 98% in virtual reality settings.
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.