DIAGNOSIS OF STUDENT CONFUSION THROUGH ARTIFICIAL INTELLIGENCE

Luciana Espã Ndola-Ulibarri, Marã A-Elena Acevedo-Mosqueda, Marco-Antonio Acevedo-Mosqueda, Sandra-Luz Gã“mez-Coronel and Ricardo Carreã‘o-Aguilera
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Luciana Espã Ndola-Ulibarri: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos†Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico
Marã A-Elena Acevedo-Mosqueda: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos†Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico
Marco-Antonio Acevedo-Mosqueda: Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos†Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico
Sandra-Luz Gã“mez-Coronel: ��Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en, Ingeniería y Tecnologías Avanzadas UPIITA, GAM, CDMX 07340, Mexico
Ricardo Carreã‘o-Aguilera: ��Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S/N, Barrio Santa Cruz, 4a. Sección Sto. Domingo Tehuantepec, C.P. 70760, Oaxaca, Mexico

FRACTALS (fractals), 2024, vol. 32, issue 01, 1-10

Abstract: Student confusion is a problem that is experienced daily in school classrooms. Teachers transfer knowledge to students without knowing if they are receiving it correctly. Being aware of whether the student correctly grasps the knowledge would be of great help since different actions could be carried out to correct this problem. The proposal of this work focuses on carrying out the diagnosis of student confusion through Machine Learning algorithms. In particular, the following algorithms were applied: Logistic Regression (LR), K-Nearest Neighbor (K-NN), Random Forest (RF) and Multi-Layer Perceptron (MLP). The metric was accuracy. The results obtained from the accuracy of each algorithm with the 5-Fold-Cross Validation validation method are 55.03% (LR), 52.98% (7-NN), 58.86% (RF) and 75.40% (MLP). An improvement in accuracy was achieved with respect to already published papers.

Keywords: Artificial Intelligence; Machine Learning; Diagnosis; Student Confusion; Electroencephalography (EEG) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X24500105

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