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Detection of Student Behavior Profiles Applying Neural Networks and Decision Trees

  • Cesar GuevaraEmail author
  • Sandra Sanchez-Gordon
  • Hugo Arias-Flores
  • José Varela-Aldás
  • David Castillo-Salazar
  • Marcelo Borja
  • Washington Fierro-Saltos
  • Richard Rivera
  • Jairo Hidalgo-Guijarro
  • Marco Yandún-Velasteguí
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1026)

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.

Keywords

Education Artificial intelligence Student Behavior profiles Neural networks Decision trees 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Cesar Guevara
    • 1
    Email author
  • Sandra Sanchez-Gordon
    • 2
  • Hugo Arias-Flores
    • 3
  • José Varela-Aldás
    • 3
  • David Castillo-Salazar
    • 3
    • 5
  • Marcelo Borja
    • 4
    • 8
  • Washington Fierro-Saltos
    • 5
  • Richard Rivera
    • 6
  • Jairo Hidalgo-Guijarro
    • 7
  • Marco Yandún-Velasteguí
    • 7
  1. 1.Mechatronics and Interactive Systems - MIST Research CenterUniversidad IndoaméricaQuitoEcuador
  2. 2.Department of Informatics and Computer ScienceEscuela Politécnica NacionalQuitoEcuador
  3. 3.SISAu Research GroupUniversidad IndoaméricaQuitoEcuador
  4. 4.Graphic Design FacultyUniversidad IndoaméricaQuitoEcuador
  5. 5.Facultad de InformáticaUniversidad Nacional de la PlataBuenos AiresArgentina
  6. 6.Escuela de Formación de Tecnólogos, Escuela Politécnica NacionalQuitoEcuador
  7. 7.Grupo de Investigación GISAT, Universidad Politécnica Estatal del CarchiTulcánEcuador
  8. 8.Facultad de Diseño y ComunicaciónUniversidad de PalermoBuenos AiresArgentina

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