Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data

Elias Dritsas, Maria Trigka, Phivos Mylonas

2024

Abstract

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 with the aid of Electroencephalography (EEG). Various ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Machine (GBM), and Neural Networks (NN), were applied to a dataset comprising EEG signals collected during e-learning sessions. Among these models, NN demonstrated the highest accuracy (90%), with precision and F1-score of 88%, a recall of 89%, and an Area Under the Curve (AUC) of 0.92 for 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.

Paper Citation

in Harvard Style

Dritsas E., Trigka M. and Mylonas P. (2024). Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data. In Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST; ISBN 978-989-758-718-4, SciTePress, pages 323-330. DOI: 10.5220/0013016000003825

in Bibtex Style

@conference{webist24,
author={Elias Dritsas and Maria Trigka and Phivos Mylonas},
title={Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data},
booktitle={Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST},
year={2024},
pages={323-330},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013016000003825},
isbn={978-989-758-718-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 20th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST
TI - Application of Machine Learning Models to Predict e-Learning Engagement Using EEG Data
SN - 978-989-758-718-4
AU - Dritsas E.
AU - Trigka M.
AU - Mylonas P.
PY - 2024
SP - 323
EP - 330
DO - 10.5220/0013016000003825
PB - SciTePress