Machine Learning Model Applied to Higher Education

Cindy Belén Espinoza Aguirre

    Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva


    Student desertion is one of the main social problems around the world. Consequently, to propose this issue, there are several studies under different circumstances or scenarios. For this reason, this research creates four datasets, which take the common variables of easy extraction to the academic process. These variables have been grouped under common characteristics such as general student profile information, admission process information, financial information, academic information, and academic performance information. Thus, the method used in this research is analytical, since it is intended to analyze each subset of data in order to identify the variables with the greatest impact on university dropout. As a result, have been identified the variables with impact on university dropout, For this, a neural network model has been implemented using Python and Keras. In conclusion, the research evidence that academic information is mostly related to college dropout related to university dropout, while admission, financial, and student profile information are not significant in detecting or predicting college dropout. However, with the data obtained, it has been shown that the prediction is not early but in many cases late, since the notes would have already been delivered to students. Therefore, future research is intended to identify the causes that originate academic problems.

    Idioma originalInglés
    Título de la publicación alojadaComplex Computational Ecosystems - 1st International Conference, CCE 2023, Proceedings
    EditoresPierre Collet, Samer El Zant, Latafat Gardashova, Ulviya Abdulkarimova
    EditorialSpringer Science and Business Media Deutschland GmbH
    Número de páginas7
    ISBN (versión impresa)9783031443541
    EstadoPublicada - 2023
    Evento1st International Conference on Complex Computational Ecosystems, CCE 2023 - Baku, Azerbaiyán
    Duración: 25 abr. 202327 abr. 2023

    Serie de la publicación

    NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volumen13927 LNCS
    ISSN (versión impresa)0302-9743
    ISSN (versión digital)1611-3349


    Conferencia1st International Conference on Complex Computational Ecosystems, CCE 2023


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