APPLICATION OF ARTIFICIAL INTELLIGENCE TECHNIQUES FOR CLASSIFICATION OF ESCAPE FROM THE TOPIC IN ESSAYS

• Autor(a) principal: Pinho, Cintia Maria de Araújo
• Data de Publicação: 2022
• Outros Autores: Gaspar, Marcos Antonio, Sassi, Renato José
• Tipo de documento: preprint
• Idioma: por
• Título da fonte: SciELO Preprints
• Texto Completo: https://preprints.scielo.org/index.php/scielo/preprint/view/3825
• topic: Redações; Avaliação automática de redações; Fuga ao tema; Inteligência artificial; Essays; Automatic essay evaluation; Go-off topic; Artificial Intelligence

The process of manual correction of essays causes some difficulties, among which we point out the time spent for correction and feedback to the student. For institutions such as elementary schools, universities and the National High School Exam in Brazil (ENEM), such activity demands time and cost for the evaluation of the texts produced. Going off-topic is one of the items evaluated in the ENEM essay that can nullify the whole essay produced by the candidate. In this context, the automatic analysis of essays with the application of techniques and methods of Natural Language Processing, Text Mining and other Artificial Intelligence (AI) techniques has shown to be promising in the process of automated evaluation of written language. The goal of this research is to compare different AI techniques for the classification of going off-topic in texts and identify the one with the best result to enable a smart correction system for essays. Therefore, computer experiments were carried out to classify these texts in order to normalize, identify patterns and classify the essays in 1,320 Brazilian Portuguese essays in 119 different topics. The results indicate that the CNN classifier (convolutional neural network) obtained greater gain in relation to the other classifiers analyzed, both in accuracy and in relation to the results of false positives, precision of metrics, recall and F1-Score. In conclusion, the solution validated in this research contributes to positively impacting the work of teachers and educational institutions, by reducing the time and costs associated with the essay evaluation process.