<?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>1607-4041</journal-id>
<journal-title><![CDATA[Revista electrónica de investigación educativa]]></journal-title>
<abbrev-journal-title><![CDATA[REDIE]]></abbrev-journal-title>
<issn>1607-4041</issn>
<publisher>
<publisher-name><![CDATA[Universidad Autónoma de Baja California, Instituto de Investigación y Desarrollo Educativo]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1607-40412023000100113</article-id>
<article-id pub-id-type="doi">10.24320/redie.2023.25.e13.5398</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Predictive Model to Identify College Students with High Dropout Rates]]></article-title>
<article-title xml:lang="es"><![CDATA[Modelo predictivo para identificar estudiantes universitarios con alto grado de deserción]]></article-title>
<article-title xml:lang="pt"><![CDATA[Modelo preditivo para identificar estudantes universitários com alto risco de evasão]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Hoyos Osorio]]></surname>
<given-names><![CDATA[Jhoan Keider]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Daza Santacoloma]]></surname>
<given-names><![CDATA[Genaro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Tecnológica de Pereira  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>25</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1607-40412023000100113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1607-40412023000100113&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1607-40412023000100113&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Decreasing student attrition rates is one of the main objectives of most higher education institutions. However, to achieve this goal, universities need to accurately identify and focus their efforts on students most likely to quit their studies before they graduate. This has given rise to a need to implement forecasting models to predict which students will eventually drop out. In this paper, we present an early warning system to automatically identify first-semester students at high risk of dropping out. The system is based on a machine learning model trained from historical data on first-semester students. The results show that the system can predict &#8220;at-risk&#8221; students with a sensitivity of 61.97%, which allows early intervention for those students, thereby reducing the student attrition rate.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen Disminuir la tasa de deserción estudiantil es uno de los principales objetivos de las instituciones de educación superior; para lograrlo, las universidades deben identificar con precisión a los estudiantes con mayor riesgo de abandonar los estudios antes de graduarse y centrar sus esfuerzos en ellos. De ahí surge la necesidad de implementar modelos predictivos capaces de identificar a los estudiantes que finalmente desertarán. En este trabajo se presenta un sistema de alerta temprana para identificar a los estudiantes de primer semestre con alto riesgo de deserción; el sistema se basa en un modelo de aprendizaje automático entrenado a partir de datos históricos de estudiantes de primer semestre. Los resultados muestran que el sistema puede identificar a los estudiantes &#8220;en riesgo&#8221; con una sensibilidad del 61.97%, lo que permite ofrecerles atención temprana y reducir la tasa de abandono.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo Reduzir a taxa de evasão estudantil é um dos principais objetivos das instituições de ensino superior; para conseguir isso, as universidades devem identificar com precisão os alunos com maior risco de abandonar os estudos antes da conclusão do curso e concentrar seus esforços neles. Daí surge a necessidade de implementar modelos preditivos capazes de identificar os alunos que acabarão por desistir. Este artigo apresenta um sistema de alerta precoce para identificar alunos do primeiro semestre com alto risco de evasão; o sistema é baseado em um modelo de aprendizagem automático treinado a partir de dados históricos de alunos do primeiro semestre. Os resultados mostram que o sistema pode identificar os alunos &#8220;em risco&#8221; com uma sensibilidade de 61.97%, o que possibilita oferecer-lhes atendimento precoce e reduzir o índice de evasão.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[dropping out]]></kwd>
<kwd lng="en"><![CDATA[college students]]></kwd>
<kwd lng="en"><![CDATA[forecasting]]></kwd>
<kwd lng="en"><![CDATA[regression analysis]]></kwd>
<kwd lng="es"><![CDATA[deserción escolar]]></kwd>
<kwd lng="es"><![CDATA[estudiante universitario]]></kwd>
<kwd lng="es"><![CDATA[previsión]]></kwd>
<kwd lng="es"><![CDATA[análisis de regresión]]></kwd>
<kwd lng="pt"><![CDATA[evasão escolar]]></kwd>
<kwd lng="pt"><![CDATA[estudante universitário]]></kwd>
<kwd lng="pt"><![CDATA[previsão]]></kwd>
<kwd lng="pt"><![CDATA[análise de regressão]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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