TY - JOUR
T1 - Eeg-based tool for prediction of university students’ cognitive performance in the classroom
AU - Ramírez-Moreno, Mauricio A.
AU - Díaz-Padilla, Mariana
AU - Valenzuela-Gómez, Karla D.
AU - Vargas-Martínez, Adriana
AU - Tudón-Martínez, Juan C.
AU - Morales-Menendez, Rubén
AU - Ramírez-Mendoza, Ricardo A.
AU - Pérez-Henríquez, Blas L.
AU - Lozoya-Santos, Jorge de J.
N1 - Funding Information:
Funding: This research was funded partially by the NOVUS program of Tecnologico de Monterrey through grant number N19106.
Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/6
Y1 - 2021/6
N2 - 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.
AB - 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.
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UR - https://www.mendeley.com/catalogue/4d3eeba6-ba10-3fd2-aa19-0633ec32bf24/
U2 - 10.3390/brainsci11060698
DO - 10.3390/brainsci11060698
M3 - Article
AN - SCOPUS:85107502162
SN - 2076-3425
VL - 11
JO - Brain Sciences
JF - Brain Sciences
IS - 6
M1 - 698
ER -