<?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>2448-878X</journal-id>
<journal-title><![CDATA[Revista latinoamericana de estudios educativos]]></journal-title>
<abbrev-journal-title><![CDATA[Rev. latinoam. estud. educ.]]></abbrev-journal-title>
<issn>2448-878X</issn>
<publisher>
<publisher-name><![CDATA[Universidad Iberoamericana (México)]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2448-878X2022000200297</article-id>
<article-id pub-id-type="doi">10.48102/rlee.2022.52.2.391</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Detección temprana del riesgo escolar. Predicción de trayectorias de rezago en la educación primaria en Uruguay mediante técnicas de machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Early Detection of School Risk. Prediction of Trajectories of School Backwardness in Primary Education in Uruguay Using Machine Learning Techniques]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cardozo]]></surname>
<given-names><![CDATA[Santiago]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Silveira]]></surname>
<given-names><![CDATA[Adrián]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Fonseca]]></surname>
<given-names><![CDATA[Bruno]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad de la República  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Uruguay</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad de la República  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Uruguay</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Administración Nacional de Educación Pública Consejo Directivo Central ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Uruguay</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>08</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>08</month>
<year>2022</year>
</pub-date>
<volume>52</volume>
<numero>2</numero>
<fpage>297</fpage>
<lpage>326</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S2448-878X2022000200297&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S2448-878X2022000200297&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S2448-878X2022000200297&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El trabajo utiliza técnicas de aprendizaje computacional (machine learning) para la estimación del riesgo escolar durante los primeros tres años de la trayectoria en la enseñanza primaria de una cohorte de alumnos uruguayos. Se utilizan tres técnicas de análisis (regresión logística, redes bayesianas y árboles de clasificación) con el propósito de identificar el riesgo de trayectorias escolares pautadas por la repetición de al menos un curso, en función de un conjunto de factores antecedentes a su transición al primer grado de enseñanza primaria. Estos factores abarcan desde las condiciones sociosanitarias al momento del nacimiento hasta la situación familiar y educativa de los alumnos sobre el final de su escolarización preprimaria. En particular, el análisis se focaliza en el poder predictivo de las habilidades captadas por la Evaluación Infantil Temprana (EIT) que se aplica próximo a la finalización de la educación inicial, sobre los cinco años de edad. Los resultados sugieren que las habilidades captadas por EIT logran identificar anticipadamente a la mayoría de los niños con riesgo educativo. Los niveles de precisión y de sensibilidad de los modelos que incluyen este factor evidencian el potencial de los sistemas de alerta temprana para detectar y prevenir situaciones de &#8220;fracaso escolar&#8221;.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract The work uses computational learning techniques (machine learning) to estimate school risk during the first three years of the trajectory in primary education of a cohort of Uruguayan students. We use three analysis techniques (logistic regression, Bayesian networks, and classification trees) to identify the risk of school trajectories based on the repetition of at least one year, based on a set of factors before the transition to the first year of primary school. These factors range from the students' socio-sanitary conditions at their birth to their family and educational situation at the end of their pre-primary schooling. In particular, the analysis focuses on the predictive power of the skills captured by the Early Childhood Assessment (EIT) that is applied close to the end of initial education, around five years of age. The results suggest that the skills captured by EIT manage to identify in advance the majority of children with educational risk. The levels of precision and sensitivity of the models that include this factor show the potential of early warning systems to detect and prevent situations of &#8220;school failure&#8221;.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[desarrollo infantil]]></kwd>
<kwd lng="es"><![CDATA[riesgo escolar]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="es"><![CDATA[repetición]]></kwd>
<kwd lng="en"><![CDATA[child development]]></kwd>
<kwd lng="en"><![CDATA[school risk]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[repetition]]></kwd>
</kwd-group>
</article-meta>
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