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DIAGNOSIS OF STUDENT CONFUSION THROUGH ARTIFICIAL INTELLIGENCE

Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos” Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico

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MARÍA-ELENA ACEVEDO-MOSQUEDA

https://orcid.org/0000-0001-8179-0398

Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos” Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico

E-mail Address: eacevedo@ipn.mx

Corresponding author.

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MARCO-ANTONIO ACEVEDO-MOSQUEDA

https://orcid.org/0000-0002-3535-1164

Instituto Politécnico Nacional, Escuela Superior de Ingeniería Mecánica y Eléctrica, Unidad Profesional “Adolfo López Mateos” Edificio Z, Tercer Piso, Zacatenco, GAM, CDMX 07738, Mexico

E-mail Address: macevedo@ipn.mx

Corresponding author.

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SANDRA-LUZ GÓMEZ-CORONEL

https://orcid.org/0000-0003-4383-9239

Instituto Politécnico Nacional, Unidad Profesional Interdisciplinaria en, Ingeniería y Tecnologías Avanzadas UPIITA, GAM, CDMX 07340, Mexico

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RICARDO CARREÑO-AGUILERA

https://orcid.org/0000-0002-6240-0152

Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S/N, Barrio Santa Cruz, 4a. Sección Sto. Domingo Tehuantepec, C.P. 70760, Oaxaca, Mexico

E-mail Address: ricardo.carreno.a@hotmail.com

Corresponding author.

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https://doi.org/10.1142/S0218348X24500105Cited by:0 (Source: Crossref)

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:

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Vol. 32, No. 01

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Received 21 June 2023

Accepted 14 November 2023

Published: November 29, 2023

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This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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DIAGNOSIS OF STUDENT CONFUSION THROUGH ARTIFICIAL INTELLIGENCE

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