<?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>2007-7467</journal-id>
<journal-title><![CDATA[RIDE. Revista Iberoamericana para la Investigación y el Desarrollo Educativo]]></journal-title>
<abbrev-journal-title><![CDATA[RIDE. Rev. Iberoam. Investig. Desarro. Educ]]></abbrev-journal-title>
<issn>2007-7467</issn>
<publisher>
<publisher-name><![CDATA[Centro de Estudios e Investigaciones para el Desarrollo Docente A.C.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2007-74672018000100246</article-id>
<article-id pub-id-type="doi">10.23913/ride.v8i16.340</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN]]></article-title>
<article-title xml:lang="en"><![CDATA[Design of a model to automate the prediction of academic performance in students of IPN]]></article-title>
<article-title xml:lang="pt"><![CDATA[Projeto de modelo para automatizar a previsão do desempenho acadêmico em estudantes do IPN]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rico Páez]]></surname>
<given-names><![CDATA[Andrés]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Sánchez Guzmán]]></surname>
<given-names><![CDATA[Daniel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Instituto Politécnico Nacional  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<volume>8</volume>
<numero>16</numero>
<fpage>246</fpage>
<lpage>266</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672018000100246&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672018000100246&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672018000100246&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen La minería de datos educativa permite extraer conocimiento útil y comprensible a partir de datos académicos para la solución de problemas acerca de diversos procesos de enseñanza y de aprendizaje. Una de las aplicaciones más populares de la minería de datos educativa es la predicción del rendimiento académico. El principal objetivo de este trabajo fue diseñar y automatizar un modelo predictivo del rendimiento académico de estudiantes del Instituto Politécnico Nacional (IPN). Para la construcción del modelo, se analizaron las calificaciones de actividades académicas y la calificación final de 94 estudiantes inscritos en una carrera de ingeniería perteneciente al IPN. Este modelo se aplicó a 86 estudiantes para predecir su rendimiento académico. Posteriormente, se compararon estas predicciones con los resultados reales obtenidos por los estudiantes al final del curso. Se obtuvieron exactitudes de las predicciones de la aprobación del curso de hasta 73%, únicamente con cinco atributos correspondientes a las calificaciones de las actividades académicas iniciales del mismo. Además, se construyó una plataforma que facilita la implementación del modelo para predecir automáticamente el desempeño académico de nuevos estudiantes. También se identificaron las principales actividades académicas que influyen en el desempeño académico a través del valor de las probabilidades del modelo. En particular, los resultados muestran que las actividades 3, 4 y 5 fueron las que influyeron de manera más significativa en la predicción de aprobación de los estudiantes que participaron en este estudio. El desarrollo de este tipo de modelos permite a las instituciones educativas predecir el rendimiento académico de sus estudiantes e identificar los principales factores que influyen en él.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Educational data mining allows extracting useful and understandable knowledge from academic data to solve problems about various teaching and learning processes. One of the most popular applications of educational data mining is the prediction of academic performance. The main objective of this work was to design and automate a predictive model of the academic performance of students of the National Polytechnic Institute (IPN). For the construction of the model, the qualifications of five academic activities and the final grade of 94 students enrolled in an Engineering career belonging to the IPN were analyzed. This model was applied to 86 students to predict their academic performance. Subsequently, these predictions were compared with the actual results obtained by the students at the end of the course. Accuracy was obtained from the predictions of the course approval of up to 73% and only with five attributes corresponding to the qualifications of the initial academic activities. In addition, a platform was built that facilitates the construction and use of the model to automatically predict the academic performance of new students. Also, the main academic activities that influenced academic performance were identified through the value of the probabilities of the model. In particular, the results showed that activities 3, 4 and 5 were those that most significantly influenced the prediction of approval of the students who participated in this study. The development of this type of models allows educational institutions to predict the academic performance of their students and identify the main factors that influence it.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A mineração de dados educacionais permite extrair conhecimento útil e compreensível de dados acadêmicos para resolver problemas sobre vários processos de ensino e aprendizagem. Uma das aplicações mais populares da mineração de dados educacionais é a previsão do desempenho acadêmico. O objetivo principal deste trabalho foi projetar e automatizar um modelo preditivo de desempenho acadêmico dos estudantes do Instituto Nacional Politécnico (IPN). Para a construção do modelo, foram analisados &#8203;&#8203;os graus de atividades acadêmicas e a nota final de 94 alunos matriculados em uma carreira de engenharia pertencente ao IPN. Este modelo foi aplicado a 86 estudantes para prever seu desempenho acadêmico. Posteriormente, essas previsões foram comparadas com os resultados reais obtidos pelos alunos no final do curso. A precisão foi obtida a partir das previsões da aprovação do curso de até 73%, com apenas cinco atributos correspondentes aos graus das atividades acadêmicas iniciais. Além disso, foi criada uma plataforma para facilitar a implementação do modelo para prever automaticamente o desempenho acadêmico de novos alunos. As principais atividades acadêmicas que influenciam o desempenho acadêmico também foram identificadas através do valor das probabilidades do modelo. Em particular, os resultados mostram que as atividades 3, 4 e 5 foram as que mais influenciaram significativamente a previsão de aprovação dos alunos que participaram deste estudo. O desenvolvimento deste tipo de modelos permite que as instituições educacionais prevejam o desempenho acadêmico de seus alunos e identifiquem os principais fatores que a influenciam.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[algoritmo Naïve Bayes]]></kwd>
<kwd lng="es"><![CDATA[minería de datos]]></kwd>
<kwd lng="es"><![CDATA[modelo predictivo]]></kwd>
<kwd lng="es"><![CDATA[probabilidades]]></kwd>
<kwd lng="es"><![CDATA[rendimiento académico]]></kwd>
<kwd lng="en"><![CDATA[Naïve Bayes algorithm]]></kwd>
<kwd lng="en"><![CDATA[data mining]]></kwd>
<kwd lng="en"><![CDATA[predictive model]]></kwd>
<kwd lng="en"><![CDATA[probabilities]]></kwd>
<kwd lng="en"><![CDATA[academic performance]]></kwd>
<kwd lng="pt"><![CDATA[algoritmo Naïve Bayes]]></kwd>
<kwd lng="pt"><![CDATA[mineração de dados]]></kwd>
<kwd lng="pt"><![CDATA[modelo preditivo]]></kwd>
<kwd lng="pt"><![CDATA[probabilidades]]></kwd>
<kwd lng="pt"><![CDATA[desempenho acadêmico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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