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.
Ramírez-Moreno, M. A., Díaz-Padilla, M., Valenzuela-Gómez, K. D., Vargas-Martínez, A., Tudón-Martínez, J. C., Morales-Menendez, R., … Lozoya-Santos, J. de J. (2021). Eeg-based tool for prediction of university students’ cognitive performance in the classroom. Brain Sciences, 11(6). https://doi.org/10.3390/brainsci11060698