<?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>S2007-74672022000100044</article-id>
<article-id pub-id-type="doi">10.23913/ride.v12i24.1196</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Modelos predictivos progresivos del rendimiento académico de estudiantes universitarios]]></article-title>
<article-title xml:lang="en"><![CDATA[Progressive Predictive Models of the Academic Performance of University Students]]></article-title>
<article-title xml:lang="pt"><![CDATA[Modelos preditivos progressivos de desempenho acadêmico de estudantes universitários]]></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-group>
<aff id="Af1">
<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>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=S2007-74672022000100044&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2007-74672022000100044&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2007-74672022000100044&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El objetivo de esta investigación fue desarrollar modelos predictivos progresivos del rendimiento académico de estudiantes universitarios de México y evaluarlos para distintas técnicas de aprendizaje automático. En este estudio se recopilaron calificaciones de actividades académicas de 260 estudiantes universitarios para crear modelos de predicción de los resultados académicos mediante técnicas de aprendizaje automático. Se construyeron los modelos en diferentes etapas a lo largo del curso y se evaluaron empleando la exactitud en la predicción de 112 estudiantes de un curso posterior. Se observó una exactitud de hasta 70.5 % en un tiempo de 21 % del total de la duración del curso. Este tipo de metodología puede ser replicada para diferentes tipos de cursos debido a que el registro de calificaciones es común en casi todos ellos. Además, esta metodología es flexible en cuanto a la elección de la etapa temporal en la cual realizar las predicciones, sin perder el compromiso con la exactitud. Así, se puede efectuar en etapas tempranas para detectar problemas con el rendimiento académico y evitar, en la medida de lo posible, la reprobación y deserción de estudiantes.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The objective of this research was to develop progressive predictive models of the academic performance of university students in Mexico and evaluate them for different machine learning techniques. In this study, grades of academic activities of 260 university students were collected to create prediction models of academic results using machine learning techniques. The models were built at different stages throughout the course and were evaluated using the accuracy of the predictions by applying it to the prediction of 112 students in a subsequent course. An accuracy of up to 70.5 % was observed in a time of 21 % of the total duration of the course. This type of methodology can be replicated for different types of courses because the recording of grades is common in almost all courses. In addition, this methodology is flexible in terms of choosing the time stage in which to make the predictions, maintaining a compromise between the accuracy of the predictions and that they be made at the earliest possible stage to detect problems with academic performance, avoiding, in as far as possible, the failure and desertion of students.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo O objetivo desta pesquisa foi desenvolver modelos preditivos progressivos do desempenho acadêmico de estudantes universitários no México e avaliá-los para diferentes técnicas de aprendizado de máquina. Neste estudo, foram coletadas notas de atividades acadêmicas de 260 estudantes universitários para criar modelos de previsão de resultados acadêmicos usando técnicas de aprendizado de máquina. Os modelos foram construídos em diferentes etapas ao longo do curso e testados usando a precisão de previsão de 112 alunos de um curso subsequente. Foi observada acurácia de até 70,5% em um tempo de 21% da duração total do curso. Esse tipo de metodologia pode ser replicada para diferentes tipos de cursos, pois o registro de notas é comum a quase todos eles. Além disso, essa metodologia é flexível quanto à escolha do momento de realização das previsões, sem perder o compromisso com a precisão. Assim, pode ser feito precocemente para detectar problemas com o desempenho acadêmico e evitar, na medida do possível, a reprovação e a evasão dos alunos.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[aprendizaje automático]]></kwd>
<kwd lng="es"><![CDATA[modelo matemático]]></kwd>
<kwd lng="es"><![CDATA[prevención]]></kwd>
<kwd lng="es"><![CDATA[rendimiento académico]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[mathematical model]]></kwd>
<kwd lng="en"><![CDATA[prevention]]></kwd>
<kwd lng="en"><![CDATA[academic performance]]></kwd>
<kwd lng="pt"><![CDATA[aprendizado de máquina]]></kwd>
<kwd lng="pt"><![CDATA[modelo matemático]]></kwd>
<kwd lng="pt"><![CDATA[prevenção]]></kwd>
<kwd lng="pt"><![CDATA[desempenho acadêmico]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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