Determining Academic Performance in Mining Engineering Students Incorporating Socioeconomic Factors through a Supervised Neural Network

Authors

  • Marco Cotrina Associate Professor, Department of Mining Engineering, National University of Trujillo, PERU
  • Jairo Marquina Assistant Professor, Department of Mining Engineering, National University of Trujillo, PERU
  • Eduardo Noriega Associate Professor , Department of Mining Engineering, National University of Trujillo, PERU
  • Jose Mamani Associate Professor , Department of Chemistry, Universidad Nacional del Altiplano de Puno, PUNO.
  • Eusebio Antonio Associate Professor , Department of Mining Engineering, National University of Trujillo, PERU
  • Solio Arango Associate Professor , Department of Mining Engineering, National University of Trujillo, PERU
  • Hans Portilla Associate Professor , Department of Metallurgical Engineering, National University of Trujillo, PERU

Keywords:

Socioeconomic factors, supervised neural networks, academic performance, Random Forest, SVM

Abstract

In recent times, artificial intelligence (AI) has become an indispensable pillar in the educational sector. One of its key applications is the prediction of students' academic performance based on personal variables such as their socioeconomic context, residence address, among others. This study introduces and develops a model based on a supervised artificial neural network designed to analyze academic performance considering socioeconomic factors. To calibrate the model, information was collected from 40 mining engineering students in the VIII and X cycles at the National University of Trujillo, Huamachuco Campus through virtual surveys, evaluating aspects such as housing type, living conditions, and food consumption patterns (including red meat, fish, fruits, and vegetables). The neural network architecture consisted of an input layer with 6 neurons, four hidden layers composed of 10, 8, 5, and 3 neurons respectively with a ReLU activation function, and an output layer with a single neuron with a sigmoid activation function. The neural network achieved an accuracy of 75.0%, and when comparing these results with other models such as Random Forest with an accuracy of 50.0% and SVM with an accuracy of 62.5%, the neural network obtained the highest accuracy compared to the other models using the same data.

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Published

24.03.2024

How to Cite

Cotrina, M. ., Marquina, J. ., Noriega, E. ., Mamani, J. ., Antonio, E. ., Arango, S. ., & Portilla, H. . (2024). Determining Academic Performance in Mining Engineering Students Incorporating Socioeconomic Factors through a Supervised Neural Network. International Journal of Intelligent Systems and Applications in Engineering, 12(3), 341–351. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5257

Issue

Vol. 12 No. 3 (2024)

Section

Research Article

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