<?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>1409-4258</journal-id>
<journal-title><![CDATA[Revista Electrónica Educare]]></journal-title>
<abbrev-journal-title><![CDATA[Educare]]></abbrev-journal-title>
<issn>1409-4258</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional. CIDE]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1409-42582023000100338</article-id>
<article-id pub-id-type="doi">10.15359/ree.27-1.14516</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelo de red neuronal artificial para predecir resultados académicos en la asignatura Matemática II]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Incio-Flores]]></surname>
<given-names><![CDATA[Fernando Alain]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Capuñay-Sanchez]]></surname>
<given-names><![CDATA[Dulce Lucero]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Estela-Urbina]]></surname>
<given-names><![CDATA[Ronald Omar]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua  ]]></institution>
<addr-line><![CDATA[Bagua ]]></addr-line>
<country>Perú</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad César Vallejo  ]]></institution>
<addr-line><![CDATA[Chiclayo ]]></addr-line>
<country>Peru</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua  ]]></institution>
<addr-line><![CDATA[Bagua ]]></addr-line>
<country>Perú</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>27</volume>
<numero>1</numero>
<fpage>338</fpage>
<lpage>359</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_arttext&amp;pid=S1409-42582023000100338&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_abstract&amp;pid=S1409-42582023000100338&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.sa.cr/scielo.php?script=sci_pdf&amp;pid=S1409-42582023000100338&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen  Objetivo:  Este artículo muestra el diseño y entrenamiento de una red neuronal artificial (RNA) para predecir resultados académicos de estudiantes de Ingeniería Civil de la Universidad Nacional Intercultural Fabiola Salazar Leguía de Bagua-Perú en la asignatura de Matemática II.  Método:  Se utilizó la metodología CRISP-DM, para recolectar los datos se emplearon encuestas, el modelo de RNA se implementó en el software Matlab utilizando el comando nnstart y dos algoritmos de aprendizaje: Scaled Conjugate Gradient (SCG) y Levenberg-Marquardt (LM), el rendimiento del modelo se evaluó mediante el error cuadrático medio y el coeficiente de correlación.  Conclusiones:  El algoritmo LM logró mejor efectividad en la predicción.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract  Objective:  This article shows the design and training of an artificial neural network (ANN) to predict academic results of Civil Engineering students of the Fabiola Salazar Leguía National Intercultural University, from Bagua-Peru, in the subject of Mathematics II.  Method:  The CRISP-DM methodology was used, surveys were conducted to collect the data, and the RNA model was implemented in the Matlab software using the nnstart command and two learning algorithms: Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM). The performance of the model was evaluated through the mean square error and the correlation coefficient.  Conclusions:  The LM algorithm achieved better prediction effectiveness.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo  Objetivo:  Este artigo mostra o projeto e o treinamento de uma rede neural artificial (RNA) para predizer resultados acadêmicos de alunos de Engenharia Civil da Universidade Nacional Intercultural Fabiola Salazar Leguía de Bagua-Peru na disciplina de Matemática II.  Método:  A metodologia CRISP-DM foi utilizada, levantamentos foram utilizados para coletar os dados, o modelo de RNA foi implementado no software Matlab usando o comando nnstart e dois algoritmos de aprendizagem: Scaled Conjugate Gradient (SCG) e Levenberg-Marquardt (LM). O desempenho do modelo foi avaliado por meio do erro quadrático médio e do coeficiente de correlação.  Conclusã:  O algoritmo LM alcançou melhor eficácia de previsão.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Red neuronal artificial]]></kwd>
<kwd lng="es"><![CDATA[rendimiento académico]]></kwd>
<kwd lng="es"><![CDATA[predicción]]></kwd>
<kwd lng="en"><![CDATA[Artificial neural network]]></kwd>
<kwd lng="en"><![CDATA[academic performance]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="pt"><![CDATA[Rede neural artificial]]></kwd>
<kwd lng="pt"><![CDATA[desempenho acadêmico]]></kwd>
<kwd lng="pt"><![CDATA[previsão]]></kwd>
</kwd-group>
</article-meta>
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