<?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>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432021000400101</article-id>
<article-id pub-id-type="doi">10.22201/fi.25940732e.2021.22.4.026</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Evaluación psicológica de profesores y alumnos mexicanos durante la pandemia de COVID-19 mediante técnicas de Machine learning]]></article-title>
<article-title xml:lang="en"><![CDATA[Machine learning approaches for psychological assessment of mexican professors and students during COVID-19 pandemic]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Navarro-Acosta]]></surname>
<given-names><![CDATA[J. Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Soto-Mendoza]]></surname>
<given-names><![CDATA[Valeria]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Saucedo-Zendejo]]></surname>
<given-names><![CDATA[Felix Raymundo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guajardo-Espinoza]]></surname>
<given-names><![CDATA[José María]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Rivera-Morales]]></surname>
<given-names><![CDATA[María Teresa]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Autónoma de Coahuila Centro de Investigación en Matemáticas Aplicadas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Autónoma de Coahuila Centro de Investigación en Matemáticas Aplicadas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af3">
<institution><![CDATA[,Universidad Autónoma de Coahuila Centro de Investigación en Matemáticas Aplicadas ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af4">
<institution><![CDATA[,Universidad Autónoma de Coahuila Facultad de Psicología ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<aff id="Af5">
<institution><![CDATA[,Universidad Autónoma de Coahuila Facultad de Psicología ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Mexico</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2021</year>
</pub-date>
<volume>22</volume>
<numero>4</numero>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432021000400101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432021000400101&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432021000400101&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: En la presente obra se describe la realización de un ejercicio de validación de resultados de una prueba psicológica aplicada a maestros y alumnos en estado de aislamiento provocado por la pandemia debida al COVID-19 en el estado de Coahuila, México. El objetivo de este trabajo es aplicar técnicas de machine learning para validar un instrumento que mide las emociones y los sentimientos negativos, así como el sesgo cognitivo o desviación de pensamiento sobre la educación y la pandemia en situación de aislamiento. Para el cumplimiento del objetivo se aplicó un instrumento en formato electrónico que se diseminó en el estado de Coahuila, fue respondido por los usuarios y se generó una base de datos, la cual después de su preprocesamiento fue analizada mediante la combinación de Random forest (RF) y Support Vector Machines (SVM); donde se obtuvo como resultado la pertinencia o no de algunos de los reactivos en las pruebas, lo que dio una validez interna al instrumento. Los resultados experimentales muestran que la metodología propuesta es capaz de seleccionar las variables predictoras más relevantes. De esta manera, se obtienen resultados satisfactorios en la clasificación y predicción de diagnósticos psicológicos globales y segmentados por características de los respondientes. Por otro lado, aunque las técnicas implementadas son robustas y confiables, estas presentan limitaciones en cuanto a la observación de los otros tipos de validez: la de constructo y la externa, entre otras; lo cual pudiera limitar su utilización. Si bien, en el campo de la psicometría existen diversas estrategias clásicas, la metodología propuesta basada en la combinación de técnicas de machine learning para el análisis y validación de este tipo de pruebas, favorece el crecimiento de opciones para mejorar los diagnósticos y en consecuencia el tratamiento de padecimientos psicológicos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This work describes the validation of the results of a psychological test applied to teachers and students in isolation due to the COVID-19 pandemic in the state of Coahuila, Mexico. The objective of this work is to apply machine learning techniques to validate an instrument that measures negative emotions and feelings, as well as cognitive bias or deviation of thinking about education and the pandemic in isolation. For the fulfillment of the objective, an instrument was applied in electronic format that was disseminated in the state of Coahuila, the users respond and the database is generated, which, after its pre-processing, is analyzed using the combination of Random Forest (RF) and Support Vector Machines (SVM); obtaining as a result the relevance or not of some of the items from the tests, thereby giving an internal validity to the instrument. The experimental results show that the proposed methodology is capable of selecting the most relevant predictor variables. In this way, satisfactory results were obtained in the classification and prediction of psychological diagnoses. On the other hand, although the implemented techniques are robust and reliable, they present limitations in terms of the observation of the other types of validity: construct, external, among others; which could limit its use. Although, in the field of psychometry there are various classic strategies, the proposed methodology based on the combination of machine learning techniques for the analysis and validation of this type of tests, favors the growth of options to improve diagnoses and consequently the treatment of psychological ailments.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Psicometría]]></kwd>
<kwd lng="es"><![CDATA[validez]]></kwd>
<kwd lng="es"><![CDATA[clasificación]]></kwd>
<kwd lng="es"><![CDATA[predicción]]></kwd>
<kwd lng="es"><![CDATA[machine learning]]></kwd>
<kwd lng="en"><![CDATA[Psychometry]]></kwd>
<kwd lng="en"><![CDATA[validity]]></kwd>
<kwd lng="en"><![CDATA[classification]]></kwd>
<kwd lng="en"><![CDATA[prediction]]></kwd>
<kwd lng="en"><![CDATA[machine learning]]></kwd>
</kwd-group>
</article-meta>
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