Resumo

PINHO, Cíntia Maria de Araújo; MOURA, Amanda Ferreira de; GASPAR, Marcos Antonio  e  NAPOLITANO, Domingos Márcio Rodrigues. IDENTIFICATION OF DISABILITIES IN EDUCATIONAL TEXTS WITH THE APPLICATION OF NATURAL LANGUAGE PROCESSING AND MACHINE LEARNING. ETD - Educ. Temat. Digit. [online]. 2022, vol.24, n.2, pp.350-372. ISSN 1676-2592.  https://doi.org/10.20396/etd.v24i2.8660061.

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.

Palavras-chave : Educational development; Knowledge management; Essay; Artificial intelligence; Technology.

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