EEG-Based Tool for Prediction of University Students’ Cognitive Performance in the Classroom
Mauricio A. Ramírez-Moreno, Mariana Díaz-Padilla, Karla D. Valenzuela-Gómez, Adriana Vargas‐Martínez, Juan C. Tudón-Martínez, Rubén Morales-Menéndez, Ricardo A. Ramírez-Mendoza, Blas L. Pérez Henríquez, Jorge de J. Lozoya-Santos
Abstract
This study presents a neuroengineering-based machine learning tool developed to predict students' performance under different learning modalities. Neuroengineering tools are used to predict the learning performance obtained through two different modalities: text and video. Electroencephalographic signals were recorded in the two groups during learning tasks, and performance was evaluated with tests. The results show the video group obtained a better performance than the text group. A correlation analysis was implemented to find the most relevant features to predict students' performance, and to design the machine learning tool. This analysis showed a negative correlation between students' performance and the (theta/alpha) ratio, and delta power, which are indicative of mental fatigue and drowsiness, respectively. These results indicate that users in a non-fatigued and well-rested state performed better during learning tasks. The designed tool obtained 85% precision at predicting learning performance, as well as correctly identifying the video group as the most efficient modality.