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Detection of Student Behavior Profiles Applying Neural Networks and Decision Trees
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Detection of Student Behavior Profiles Applying Neural Networks and Decision Trees
Journal
Advances in Intelligent Systems and Computing
Date Issued
2020
Author(s)
Guevara Maldonado, César Byron
Centro de investigación en Mecatrónica y Sistemas Interactivos
Sanchez-Gordon S.
Arias Flores, Hugo Patricio
Centro de investigación en Mecatrónica y Sistemas Interactivos
Varela Aldas, José
Centro de Investigación de Ciencias Humanas y de la Educación
Castillo Salazar, David Ricardo
Facultad de Ciencias de la Educación
Borja M.
Fierro-Saltos W.
Rivera R.
Hidalgo-Guijarro J.
Yandún-Velasteguí M.
Type
Conference Paper
DOI
10.1007/978-3-030-27928-8_90
URL
https://cris.indoamerica.edu.ec/handle/123456789/8928
Abstract
Education worldwide is a significant aspect for the development of the peoples and much more in developing countries such as those in Latin America, where less than 22% of its inhabitants have higher education. Research in this field is a matter of interest for each of the governments to improve education policies. Therefore, the analysis of data on the behavior of a student in an educational institution is of utmost importance, because multiple aspects of progress or student dropout rates during their professional training period can be identified. The most important variables to identify the student’s behavior are the socio-economic ones, since the psychological state and the economic deficiencies that the student faces while is studying can be detected. This data provides grades, scholarships, attendance and information on student progress. During the first phase of the study, all the information is analyzed and it is determined which provides relevant data to develop a profile of a student behavior, as well as the pre-processing of the data obtained. In this phase, voracious algorithms are applied for the selection of attributes, such as greedy stepwise, Chi-squared test, Anova, RefiefF, Gain Radio, among others. In this work, we apply the artificial intelligence techniques, the results obtained are compared to generate a normal and unusual behavior of each student according to their professional career. In addition, the most optimal model that has had a higher accuracy percentage, false positive rate, false negative rate and mean squared error in the tests results are determined. © Springer Nature Switzerland AG 2020.
Subjects
Algorithm; Evolutiona...
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Acquisition Date
Dec 6, 2024
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