<?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>2218-3620</journal-id>
<journal-title><![CDATA[Revista Universidad y Sociedad]]></journal-title>
<abbrev-journal-title><![CDATA[Universidad y Sociedad]]></abbrev-journal-title>
<issn>2218-3620</issn>
<publisher>
<publisher-name><![CDATA[Editorial "Universo Sur"]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2218-36202022000200297</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Applying Bayesian networks in student dropout data]]></article-title>
<article-title xml:lang="es"><![CDATA[Aplicación de redes bayesianas en datos de abandono estudiantil]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Oviedo Bayas]]></surname>
<given-names><![CDATA[Byron]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gómez Gómez]]></surname>
<given-names><![CDATA[Jorge]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zambrano Vega]]></surname>
<given-names><![CDATA[Cristian]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morán Morán]]></surname>
<given-names><![CDATA[Evelym Ruth]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Técnica Estatal de Quevedo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Ecuador</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de Córdoba  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>04</month>
<year>2022</year>
</pub-date>
<volume>14</volume>
<numero>2</numero>
<fpage>297</fpage>
<lpage>304</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_arttext&amp;pid=S2218-36202022000200297&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_abstract&amp;pid=S2218-36202022000200297&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.sld.cu/scielo.php?script=sci_pdf&amp;pid=S2218-36202022000200297&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT This paper presents a proposal to implement a cluster method that best engages the educational data (socio-economic, academic achievement and dropouts) at the Engineering Faculty of Quevedo State Technical University. The use of graphical probabilistic models in the field of education has been proposed for this research. To complete the student diagnosis, and to predict their behavior as well, an analysis of such Bayesian networks learning models, as PC, K2, and EM optimization was made first. There should be a test for each case where the probability is measured in every model using propagation algorithms. Then, probability logarithm is applied to each case and the results are added in each model to determine the best fit for the proposed. The results of this research will help raise awareness of the various factors affecting students&#8217; performance. Besides, this will allow institutional authorities to identify mechanisms for improving retention index and students&#8217; academic achievement, what serves the improvement of quality indicators in face of institutional and program evaluation and accreditation processes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN Este artículo presenta una propuesta para implementar un método de conglomerados que mejor involucre los datos educativos (socioeconómicos, rendimiento académico y deserción) en la Facultad de Ingeniería de la Universidad Técnica del Estado de Quevedo. Para esta investigación se ha propuesto el uso de modelos gráficos probabilísticos en el campo de la educación. Para completar el diagnóstico de los estudiantes, y también para predecir su comportamiento, primero se realizó un análisis de modelos de aprendizaje de redes bayesianas, como la optimización de PC, K2 y EM. Debe haber una prueba para cada caso donde la probabilidad se mide en cada modelo usando algoritmos de propagación. Luego, se aplica el logaritmo de probabilidad a cada caso y los resultados se suman en cada modelo para determinar el mejor ajuste para el modelo propuesto. Los resultados de esta investigación ayudarán a crear conciencia sobre los diversos factores que afectan el desempeño de los estudiantes. Además, esto permitirá a las autoridades institucionales identificar mecanismos para mejorar el índice de retención y rendimiento académico de los estudiantes, que sirva para mejorar los indicadores de calidad de cara a los procesos de evaluación y acreditación institucional y de programas.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Bayesian networks]]></kwd>
<kwd lng="en"><![CDATA[K2]]></kwd>
<kwd lng="en"><![CDATA[PC]]></kwd>
<kwd lng="en"><![CDATA[EM]]></kwd>
<kwd lng="en"><![CDATA[academic performance]]></kwd>
<kwd lng="es"><![CDATA[Redes bayesianas]]></kwd>
<kwd lng="es"><![CDATA[K2]]></kwd>
<kwd lng="es"><![CDATA[PC]]></kwd>
<kwd lng="es"><![CDATA[EM]]></kwd>
<kwd lng="es"><![CDATA[rendimiento académico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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