Uaffect: Um Modelo para Predição de Estados Afetivos em Ambientes Educacionais a partir de Históricos de Contextos

• Sandro O. Dorneles IFRS
• Luís Guilherme Eich UNISINOS
• Débora Nice Ferrari Barbosa Universidade Feevale
• Rosemary Francisco UNISINOS
• Jorge L. V. Barbosa UNISINOS

DOI: https://doi.org/10.5753/sbie.2023.235233

Abstract

Studies indicate the growth and relevance of recognition systems of affective states in different areas. It is also known that there is a strong relationship between emotions and learning. So, what is the importance of context information in predicting affective states in educational environments? Thus, this article proposes Uaffect, a computational model that uses historical contexts formed in educational environments to classify and predict affective states. The model was evaluated from a quasi experimental study with 25 students. The accuracy results found in the best scenario are 89% for positive affective states and 61% for negative affective states. The tests carried out showed evidences that indicate the possibility of using historical contexts in the classification and prediction of affective states in educational environments, which can be used to provide intelligent services that assist in decision making in educational planning and affective regulation of the student.

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