| E. Dritsas, M. Trigka, Ph. Mylonas |
| Application of Machine Learning Models to Predict E-Learning Engagement Using EEG Data |
| 20th International Conference on Web Information Systems and Technologies (WEBIST 2024), 17-19 November 2024, Porto, Portugal |
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ABSTRACT
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| The rapid evolution of e-learning platforms necessitates the development of innovative methods to enhance learner engagement. This study leverages machine learning (ML) techniques and models to predict e-learning engagement using EEG data. Various ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Networks (NNs), were applied to a dataset comprising Electroencephalography (EEG) signals collected during e-learning sessions. Among these models, NN demonstrated the highest Accuracy of 90%, Precision and F1-score of 88%, Recall of 89%, and the Area Under the Curve (AUC) of 0.92 within predicting engagement levels. The results underscore the potential of EEG-based analysis combined with advanced ML techniques to optimize e-learning environments by accurately monitoring and responding to learner engagement.
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| 17 November , 2024 |
| E. Dritsas, M. Trigka, Ph. Mylonas, "Application of Machine Learning Models to Predict E-Learning Engagement Using EEG Data", 20th International Conference on Web Information Systems and Technologies (WEBIST 2024), 17-19 November 2024, Porto, Portugal |
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