<?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>1990-8644</journal-id>
<journal-title><![CDATA[Conrado]]></journal-title>
<abbrev-journal-title><![CDATA[Conrado]]></abbrev-journal-title>
<issn>1990-8644</issn>
<publisher>
<publisher-name><![CDATA[Editorial Universo Sur]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1990-86442021000600305</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo predictivo del progreso en el aprendizaje de los estudiantes de uniminuto aplicando técnicas de machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Predictive model of the learning progress of uniminuto students applying machine learning techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bautista Cañón]]></surname>
<given-names><![CDATA[Elmer]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Quirama Salamanca]]></surname>
<given-names><![CDATA[Jenny E.]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Bautista Cañón]]></surname>
<given-names><![CDATA[Edilfonso]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Corporación Universitaria UNIMINUTO  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>17</volume>
<numero>83</numero>
<fpage>305</fpage>
<lpage>310</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S1990-86442021000600305&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S1990-86442021000600305&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S1990-86442021000600305&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Esta investigación tiene como propósito validar la hipótesis donde se propone explicar la relación del progreso del aprendizaje de los estudiantes de UNIMINUTO en las pruebas de salida, con respecto a las variables de las pruebas de entrada a la educación superior, a partir del desarrollo de un modelo predictivo de aprendizaje supervisado. Para el desarrollo del proyecto se estableció una metodología con 4 fases, análisis descriptivo de los datos para 10505 instancias con 69 variables, análisis predictivo, análisis prescriptivo y aplicaciones del modelo. Como resultado se desarrollaron tres modelos predictivos con los algoritmos de regresión logística, máquinas de vector soporte y redes neuronales, los cuales mostraron una eficiencia cercana al 75% y la evaluación de las métricas de precisión, recall y f1, con porcentajes de eficiencia similares. En conclusión, se logró desarrollar 3 modelos predictivos del progreso del aprendizaje de los estudiantes de UNIMINUTO, a partir de la información suministrada por el ICFES, que relaciona las variables de entrada con las variables de salida de la educación superior en Colombia.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The purpose of this research is to validate the hypothesis where it is proposed to explain the relationship of the learning progress of UNIMINUTO students in the exit tests, with respect to the variables of the entrance tests to higher education, based on the development of a supervised learning predictive model. For the development of the project, a methodology with 4 phases was established, descriptive analysis of the data for 10,505 instances with 69 variables, predictive analysis, prescriptive analysis and applications of the model. As a result, three predictive models were developed with logistic regression algorithms, support vector machines and neural networks, which showed an efficiency close to 75% and the evaluation of the precision metrics, recall and f1, with similar percentages of efficiency. In conclusion, it was possible to develop 3 predictive models of the learning progress of UNIMINUTO students, based on the information provided by ICFES, which relates the input variables with the output variables of higher education in Colombia.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Aprendizaje supervisado]]></kwd>
<kwd lng="es"><![CDATA[modelo predictivo]]></kwd>
<kwd lng="es"><![CDATA[regresión logística]]></kwd>
<kwd lng="es"><![CDATA[máquinas de vector soporte]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales]]></kwd>
<kwd lng="es"><![CDATA[valor agregado educación]]></kwd>
<kwd lng="en"><![CDATA[Supervised learning]]></kwd>
<kwd lng="en"><![CDATA[predictive model]]></kwd>
<kwd lng="en"><![CDATA[logistic regression]]></kwd>
<kwd lng="en"><![CDATA[support vector machines]]></kwd>
<kwd lng="en"><![CDATA[neural networks]]></kwd>
<kwd lng="en"><![CDATA[value added education]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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