<?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>1676-2592</journal-id>
<journal-title><![CDATA[ETD Educação Temática Digital]]></journal-title>
<abbrev-journal-title><![CDATA[ETD - Educ. Temat. Digit.]]></abbrev-journal-title>
<issn>1676-2592</issn>
<publisher>
<publisher-name><![CDATA[Universidade Estadual de Campinas - UNICAMP]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1676-25922022000200350</article-id>
<article-id pub-id-type="doi">10.20396/etd.v24i2.8660061</article-id>
<title-group>
<article-title xml:lang="pt"><![CDATA[IDENTIFICAÇÃO DE DEFICIÊNCIAS EM TEXTOS EDUCACIONAIS COM A APLICAÇÃO DE PROCESSAMENTO DE LINGUAGEM NATURAL E APRENDIZADO DE MÁQUINA]]></article-title>
<article-title xml:lang="en"><![CDATA[IDENTIFICATION OF DISABILITIES IN EDUCATIONAL TEXTS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING]]></article-title>
<article-title xml:lang="es"><![CDATA[IDENTIFICACIÓN DE DEFICIENCIAS EN TEXTOS EDUCATIVOS CON LA APLICACIÓN DE PROCESAMIENTO DEL LENGUAJE NATURAL Y APRENDIZAJE AUTOMÁTICO]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pinho]]></surname>
<given-names><![CDATA[Cíntia Maria de Araújo]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Moura]]></surname>
<given-names><![CDATA[Amanda Ferreira de]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Gaspar]]></surname>
<given-names><![CDATA[Marcos Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Napolitano]]></surname>
<given-names><![CDATA[Domingos Márcio Rodrigues]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="A01">
<institution><![CDATA[,Universidade Nove deJulho Pós-graduação em Informática e Gestão do Conhecimento Centro Estadual de Educação Tecnológica Paula Souza]]></institution>
<addr-line><![CDATA[Ribeirão Pires SP]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="A02">
<institution><![CDATA[,Universidade Nove de Julho Pós-graduada em Educação a Distância ]]></institution>
<addr-line><![CDATA[São Paulo SP]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="A03">
<institution><![CDATA[,Universidade de São Paulo Universidade Nove de Julho Programa de Pós-graduação em Informática e Gestão do Conhecimento]]></institution>
<addr-line><![CDATA[São Paulo SP]]></addr-line>
<country>Brasil</country>
</aff>
<aff id="A04">
<institution><![CDATA[,Universidade Nove de Julho Programa de Pós-graduação em Informática e Gestão do Conhecimento ]]></institution>
<addr-line><![CDATA[São Paulo SP]]></addr-line>
<country>Brasil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>05</month>
<year>2022</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>05</month>
<year>2022</year>
</pub-date>
<volume>24</volume>
<numero>2</numero>
<fpage>350</fpage>
<lpage>372</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_arttext&amp;pid=S1676-25922022000200350&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_abstract&amp;pid=S1676-25922022000200350&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://educa.fcc.org.br/scielo.php?script=sci_pdf&amp;pid=S1676-25922022000200350&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="pt"><p><![CDATA[RESUMO A correção de textos educacionais como redações e questões discursivas é uma tarefa importante; além disso, diversas escolas têm exigido a intensificação da atividade da escrita para a evolução do discente. O esforço despendido para a correção, entretanto, pode aumentar a carga de trabalho do professor ou até mesmo gerar custos adicionais, bem como um longo tempo de correção para instituições como o MEC (Ministério da Educação), que é responsável pela aplicação do ENEM (Exame Nacional do Ensino Médio). Em 2019 foi anunciada pelo MEC a tendência de o ENEM se tornar digital, trazendo novas possibilidades para a análise e avaliação das redações elaboradas pelos estudantes. Nesse contexto, algumas técnicas de inteligência Artificial para análise de textos educacionais têm-se revelado úteis no processo de avaliação automática da linguagem escrita. Assim, o objetivo desta pesquisa é analisar textos, empregando para tanto as técnicas de Processamento de Linguagem Natural e Aprendizado de Máquina para identificar deficiências em textos educacionais. Esta pesquisa experimental consistiu na classificação de 695 redações elaboradas em língua portuguesa em 20 temas. Os resultados demonstraram que as técnicas empregadas possibilitaram a identificação de redações cujo conteúdo foge à temática proposta na prova, entre outras informações importantes para que o docente possa identificar falhas na escrita da redação, tais como a coesão textual ou texto insuficiente. Os resultados esperados com a aplicação da solução desenvolvida neste experimento buscam otimizar o trabalho do professor, bem como reduzir o tempo e o custo do processo de avaliação de textos educativos.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT The correction of educational texts such as essays and discursive questions is an important task, in addition, several schools have demanded the intensification of the activity of writing for the evolution of the student. However, the effort spent on correction can increase the workload of the teacher or even generate additional costs and a long correction time for institutions such as the MEC (Ministry of Education), which is responsible for the application of ENEM (National Examination for Education Medium). In 2019, MEC announced the trend of ENEM to become digital, bringing new possibilities for evaluating and analyzing the essays prepared by students. In this context, some artificial intelligence techniques for analyzing educational texts have proven to be useful in the process of automatic assessment of written language. Thus, the objective of this research is to analyze texts using the techniques of Natural Language Processing and Machine Learning to identify deficiencies in educational texts. This experimental research consisted of the classification of 695 essays prepared in Portuguese in 20 themes. The results showed that the techniques employed made it possible to identify essays whose content differs from the theme proposed in the test, among other important information so that the teacher can identify flaws in the writing of the essay, such as textual cohesion or insufficient text. The expected results with the application of the solution developed in this experiment seek to optimize the work of the teacher, reducing the time and cost of the process of evaluating educational texts.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN La corrección de textos educativos como redacciones y preguntas discursivas es una tarea importante, especialmente porque varias escuelas han exigido la intensificación de la actividad de la escritura para la evolución del estudiante. El esfuerzo dedicado a la corrección puede aumentar la carga de trabajo del maestro o incluso generar costos adicionales, además del largo tiempo de corrección para instituciones como el MEC (Ministerio de Educación), que es responsable de aplicar el ENEM (Examen Nacional del escuela secundaria). En 2019, MEC anunció la tendencia de ENEM a convertirse en digital, brindando nuevas posibilidades para evaluar y analizar las redacciones preparadas por los estudiantes. En este contexto, algunas técnicas de Inteligencia Artificial para el análisis automático de textos educativos han demostrado ser útiles en el proceso de evaluación automática del lenguaje escrito. El objetivo de esta investigación es analizar textos utilizando las técnicas de Procesamiento del lenguaje natural y Aprendizaje automático para identificar deficiencias en los textos educativos. Esta investigación experimental consistió en la clasificación de 695 redacciones en portugués en 20 temas. Los resultados mostraron que las técnicas empleadas permitieron identificar salas de redacción cuyo contenido difiere del tema propuesto en la prueba, entre otra información importante para que el maestro pueda identificar fallas en la redacción, como la cohesión textual o texto insuficiente. Los resultados esperados con la aplicación de la solución desarrollada en este experimento buscan optimizar el trabajo del profesor, reduciendo el tiempo y el costo del proceso de evaluación de textos educativos.]]></p></abstract>
<kwd-group>
<kwd lng="pt"><![CDATA[Desenvolvimento educacional]]></kwd>
<kwd lng="pt"><![CDATA[Gestão do Conhecimento]]></kwd>
<kwd lng="pt"><![CDATA[Redação]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência Artificial]]></kwd>
<kwd lng="pt"><![CDATA[Tecnologia]]></kwd>
<kwd lng="en"><![CDATA[Educational development]]></kwd>
<kwd lng="en"><![CDATA[Knowledge management]]></kwd>
<kwd lng="en"><![CDATA[Essay]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Technology]]></kwd>
<kwd lng="es"><![CDATA[Desarrollo educativo]]></kwd>
<kwd lng="es"><![CDATA[Gestión del conocimiento]]></kwd>
<kwd lng="es"><![CDATA[Redacción]]></kwd>
<kwd lng="es"><![CDATA[Inteligencia Artificial]]></kwd>
<kwd lng="es"><![CDATA[Tecnología]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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