<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0121-750X</journal-id>
<journal-title><![CDATA[Ingeniería]]></journal-title>
<abbrev-journal-title><![CDATA[ing.]]></abbrev-journal-title>
<issn>0121-750X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Distrital Francisco José de Caldas]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0121-750X2023000100202</article-id>
<article-id pub-id-type="doi">10.14483/23448393.19514</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Prediction of University-Level Academic Performance through Machine Learning Mechanisms and Supervised Methods]]></article-title>
<article-title xml:lang="es"><![CDATA[Predicción del rendimiento académico universitario mediante mecanismos de aprendizaje automático y métodos supervisados]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Contreas-Bravo]]></surname>
<given-names><![CDATA[Leonardo Emiro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Nieves-Pimiento]]></surname>
<given-names><![CDATA[Nayive]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Guerrero]]></surname>
<given-names><![CDATA[Karolina]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Distrital Francisco José de Caldas  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad ECCI  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Militar Nueva Granada  ]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2023</year>
</pub-date>
<volume>28</volume>
<numero>1</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0121-750X2023000100202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0121-750X2023000100202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0121-750X2023000100202&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Context:  In the education sector, variables have been identified which considerably affect students&#8217; academic performance. In the last decade, research has been carried out from various fields such as psychology, statistics, and data analytics in order to predict academic performance.  Method:  Data analytics, especially through Machine Learning tools, allows predicting academic performance using supervised learning algorithms based on academic, demographic, and sociodemographic variables. In this work, the most influential variables in the course of students&#8217; academic life are selected through wrapping, embedded, filter, and assembly methods, as well as the most important characteristics semester by semester using Machine Learning algorithms (Decision Trees, KNN, SVC, Naive Bayes, LDA), which were implemented using the Python language.  Results:  The results of the study show that the KNN is the model that best predicts academic performance for each of the semesters, followed by Decision Trees, with precision values that oscillate around 80 and 78,5% in some semesters.  Conclusions:  Regarding the variables, it cannot be said that a student&#8217;s per-semester academic average necessarily influences the prediction of academic performance for the next semester. The analysis of these results indicates that the prediction of academic performance using Machine Learning tools is a promising approach that can help improve students&#8217; academic life allow institutions and teachers to take actions that contribute to the teaching-learning process.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Contexto: En el sector educativo se han identificado variables que inciden considerablemente en el rendimiento academico de los estudiantes. En la ultima decada se han llevado a cabo investigaciones desde diversos campos como la psicologia, la estadistica y el analisis de datos con el fin de predecir el rendimiento academico.  Metodo: La analitica de datos, especialmente a traves de herramientas de Machine Learning, permite predecir el rendimiento academico utilizando algoritmos de aprendizaje supervisado basados en variables academicas, demograficas y sociodemograficas. En este trabajo se seleccionan las variables mas influyentes en el transcurso de la vida academica de los estudiantes mediante metodos de filtro, embebidos, y de ensamble, asi como las caracteristicas mas importantes semestre a semestre utilizando algoritmos de Machine Learning (arbol de decision, KNN, SVC, Naive Bayes, LDA), implementados en el lenguaje Python.  Resultados: Los resultados del estudio muestran que el KNN es el modelo que mejor predice el rendimiento academico para cada uno de los semestres, seguido de los arboles de decision, con valores de precision que oscilan alrededor del 80 y 78,5% en algunos semestres.  Conclusiones: Con respecto a las variables, no se puede decir que el promedio academico semestral de un estudiante influya necesariamente en la prediccion del rendimiento academico del siguiente semestre. El analisis de estos resultados indica que la prediccion del rendimiento academico utilizando herramientas de Machine Learning es un enfoque promisorio que puede ayudar a mejorar la vida academica de los estudiantes y permitir a las instituciones y a los docentes adoptar acciones que ayuden al proceso de ensenanza-aprendizaje.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[educational data analysis]]></kwd>
<kwd lng="en"><![CDATA[Machine Learning]]></kwd>
<kwd lng="en"><![CDATA[higher education]]></kwd>
<kwd lng="es"><![CDATA[analisis de datos educativos]]></kwd>
<kwd lng="es"><![CDATA[Machine Learning]]></kwd>
<kwd lng="es"><![CDATA[educacion superior]]></kwd>
</kwd-group>
</article-meta>
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