<?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>S2007-74672022000100035</article-id>
<article-id pub-id-type="doi">10.23913/ride.v12i24.1180</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Algoritmos de aprendizaje automático para la predicción del logro académico]]></article-title>
<article-title xml:lang="en"><![CDATA[Machine Learning Algorithms for Predicting of Academic Achievement]]></article-title>
<article-title xml:lang="pt"><![CDATA[Algoritmos de aprendizado de máquina para previsão de desempenho acadêmico]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Morales Hernández]]></surname>
<given-names><![CDATA[Miguel Ángel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González Camacho]]></surname>
<given-names><![CDATA[Juan Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Robles Vásquez]]></surname>
<given-names><![CDATA[Héctor]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Valle Paniagua]]></surname>
<given-names><![CDATA[David H. del]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Durán Moreno]]></surname>
<given-names><![CDATA[José Rafael]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Colegio de Postgraduados Campus Montecillo ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Colegio de Postgraduados Campus Montecillo ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Planeación y Evaluación del Consejo Nacional de Fomento Educativo  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Colegio de Postgraduados Campus Montecillo ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,rduran1091@gmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2022</year>
</pub-date>
<volume>12</volume>
<numero>24</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2007-74672022000100035&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672022000100035&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672022000100035&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En esta investigación se implementaron dos clasificadores de aprendizaje automático, una red neuronal multicapa (perceptrón multicapa [MLP]) y un modelo de potenciación del gradiente (GB), para predecir el grado de logro académico en las asignaturas de español y matemáticas de alumnos de sexto de primaria (2008) y tercero de secundaria (2011) con base en variables contextuales obtenidas de los Exámenes Nacionales del Logro Académico en Centros Escolares (Enlace) del estado de Tlaxcala, México. Se consideraron 13 variables de entrada y la importancia relativa de éstas, se determinó por medio del algoritmo bosque aleatorio (RF). Los clasificadores MLP y GB se entrenaron y probaron con un conjunto de datos de 11 036 registros de estudiantes que permanecieron en el sistema escolar de 2008 a 2011. Los modelos se entrenaron y probaron en predicción para 2008 y 2011. En español MLP fue superior a GB con una precisión global de clasificación (PG) de 70.1 % en 2008 y 61.1 % en 2011. GB obtuvo mejores resultados en matemáticas con una PG de 68.8 % en 2008 y 63.5 % en 2011. Se observó que el puntaje en español tiene una fuerte asociación con el grado de logro académico en matemáticas. Los puntajes en español y matemáticas tuvieron mayor importancia relativa con respecto a los factores contextuales analizados como: sexo, beca, turno de la escuela. En la población de alumnos analizada se observó que en español y matemáticas la proporción de mujeres es mayor a la proporción de hombres en los grados de logro académico elemental, y bueno o excelente; en contraste, en ambas asignaturas esta proporción se invierte con el grado de logro insuficiente.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this research, two machine learning classifiers were implemented, a multilayer perceptron (MLP) and a gradient boosting model (GB), to predict the degree of academic achievement in Spanish and mathematics of basic education students in two stages, sixth of primary (2008) and third of secondary (2011), based on contextual variables obtained from the Enlace test of the state of Tlaxcala, Mexico. Thirteen input variables were considered. The relative importance of these was determined by the random forest (RF) classifier. MLP and GB classifiers were trained and tested with a dataset of 11 036 records of students who remained in the school system from 2008 to 2011. The models were trained and tested in prediction for 2008 and 2011. In Spanish MLP outperformed GB with a global classification accuracy (PG) of 70.1 % in 2008 and 61.1 % in 2011. GB obtained better performance in mathematics with a PG of 68.8 % in 2008 and 63.5 % in 2011. It was observed that the score in Spanish has a strong association with the degree of academic achievement in mathematics. Scores in Spanish and mathematics have greater relative importance with respect to contextual factors considered as sex, scholarship, school shift, and so on. In the population of students analyzed, it is observed that, in Spanish and mathematics, the proportion of women is higher than the proportion of men in achievement levels 1 (elementary) and 2 (good or excellent); in contrast, in both subjects this proportion is reversed at achievement level 0 (insufficient).]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Nesta pesquisa, dois classificadores de aprendizado de máquina, uma rede neural multicamada (multilayer perceptron [MLP]) e um modelo de potenciação de gradiente (GB), foram implementados para prever o grau de desempenho acadêmico nas disciplinas de espanhol e matemática de alunos do ensino médio. sexta série (2008) e terceira série (2011) com base em variáveis contextuais obtidas nos Exames Nacionais de Desempenho Acadêmico nas Escolas (Enlace) do estado de Tlaxcala, México. 13 variáveis de entrada foram consideradas e sua importância relativa foi determinada usando o algoritmo Random Forest (RF). Os classificadores MLP e GB foram treinados e testados com um conjunto de dados de 11.036 prontuários de alunos que permaneceram na rede escolar de 2008 a 2011. Os modelos foram treinados e testados em previsão para 2008 e 2011. Em espanhol, o MLP foi superior ao GB com uma precisão geral de notas (GP) de 70,1% em 2008 e 61,1% em 2011. GB teve um desempenho melhor em matemática com um GP de 68,8% em 2008 e 63,5% em 2011. A pontuação em espanhol mostrou ter uma forte associação com o grau de desempenho acadêmico em matemática. Os escores em espanhol e matemática tiveram maior importância relativa em relação aos fatores contextuais analisados como: gênero, escolaridade, turno escolar. Na população de alunos analisada, observou-se que em espanhol e matemática a proporção de mulheres é maior do que a proporção de homens no ensino fundamental e nas notas de desempenho acadêmico bom ou excelente; e essa proporção se inverte com o grau de realização insuficiente.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje supervisado]]></kwd>
<kwd lng="es"><![CDATA[árboles de decisión]]></kwd>
<kwd lng="es"><![CDATA[contexto escolar]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales artificiales]]></kwd>
<kwd lng="es"><![CDATA[validación cruzada]]></kwd>
<kwd lng="en"><![CDATA[supervised learning]]></kwd>
<kwd lng="en"><![CDATA[decision trees]]></kwd>
<kwd lng="en"><![CDATA[school context]]></kwd>
<kwd lng="en"><![CDATA[artificial neural networks]]></kwd>
<kwd lng="en"><![CDATA[cross validation]]></kwd>
<kwd lng="pt"><![CDATA[aprendizagem supervisionada]]></kwd>
<kwd lng="pt"><![CDATA[árvores de decisão]]></kwd>
<kwd lng="pt"><![CDATA[contexto escolar]]></kwd>
<kwd lng="pt"><![CDATA[redes neurais artificiais]]></kwd>
<kwd lng="pt"><![CDATA[validação cruzada]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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