Deep Neural Network (DNN) Applied to the Analysis of Student Dropout in a Higher Education Institution
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Copyright (c) 2022 Investigación e Innovación en Ingenierías
This work is licensed under a Creative Commons Attribution 4.0 International License.
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- Submited: December 22, 2021
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Published: June 30, 2022
Abstract
This paper reports the design of a deep neural network (DNN) focused to predict patterns of student dropout at the UPTC University of Colombia. We apply specialized artificial intelligence (AI) algorithms for the implementation of the DNN specifically were using a learning machine for classification and clustering task. Additionally, a dataset containing 17 attributes of 3000 academically active students was structured; the data set has been prepared to be trained as an entry to the neural network. The final result of the research reveals a predictive model trained through the DNN validated by several quality metrics that demonstrate reliability and accuracy of the results accomplished through the model.
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How to Cite
[1]
M. J. Suárez Barón, J. S. Gonzalez Sanabria, and J. E. Espindola Diaz, “Deep Neural Network (DNN) Applied to the Analysis of Student Dropout in a Higher Education Institution”, Investigación e Innovación en Ingenierías, vol. 10, no. 1, pp. 202–214, Jun. 2022.
