Neutrosophic Analysis of Disruptive Behaviors in Children Aged 6 to 8 Years and Its Impact on Academic Performance: Application of the Plithogenic Hypothesis Method

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María Leonor Atiencie Gutiérrez
Natalia Elizabeth Lojano Patiño
Erika Gabriela Alava Jimènez
Cindy Gabriela Ortega Garcia

Abstract

This study examines disruptive behaviors in 6- to 8-year-old children at the Abdón Calderón Educational Unit, located in Guayaquil, Guayas, Ecuador, and their influence on academic performance. The relevance of this topic lies in its impact on learning and the school environment, especially in urban contexts such as Guayaquil, where educational challenges are pressing. Although numerous studies have addressed disruptive behaviors, few have integrated uncertainty and the multiple factors that characterize them, leaving a gap in multidimensional analysis. To address this limitation, the plithogenic hypothesis method, based on neutrosophic, was applied, which models indeterminacy and contradictions in behavioral data. The results indicate that behaviors such as impulsivity and inattention in students at the Abdón Calderón Educational Unit negatively affect performance in mathematics and reading. This work contributes to the educational field by proposing an innovative analytical framework that captures the complexity of these interactions, also offering practical intervention strategies for teachers and psychologists in Ecuadorian contexts. Thus, the study enriches educational theory and provides applicable tools to improve academic performance in local schools.

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Neutrosophic Analysis of Disruptive Behaviors in Children Aged 6 to 8 Years and Its Impact on Academic Performance: Application of the Plithogenic Hypothesis Method. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 41, 79-98. https://fs.unm.edu/NCML2/index.php/112/article/view/901
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How to Cite

Neutrosophic Analysis of Disruptive Behaviors in Children Aged 6 to 8 Years and Its Impact on Academic Performance: Application of the Plithogenic Hypothesis Method. (2025). Neutrosophic Computing and Machine Learning. ISSN 2574-1101, 41, 79-98. https://fs.unm.edu/NCML2/index.php/112/article/view/901