Modelo de clasificación basado en chatbot y algoritmos no supervisados para determinar programas de intervención psicológica en estudiantes universitarios peruanos

• Autores: Huamán Labán, Joyse Baldwin, Gómez Apaza, Roel Dante
• Formato: tesis de maestría
• Fecha de Publicación: 2022
• Institución: Universidad Peruana Unión
• Repositorio: UPEU-Tesis
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.upeu.edu.pe:20.500.12840/5629
• Enlace del recurso: http://repositorio.upeu.edu.pe/handle/20.500.12840/5629
• Nivel de acceso: acceso abierto
• Materia: Automated classification; Artificial intelligence; Grouping; K-means; University tutoring; http://purl.org/pe-repo/ocde/ford#2.02.04

A strategy that supports the student’s academic and personal formation is that university consider tutoring as a mechanism that supports with favorable results to fight against the desertion of students. However, there are related prob-lems in performing student segmentation and conducting psychological interven-tions. The objective was to formulate a classification model for intervention pro-grams in university students based on unsupervised algorithms. For this, we car-ried out a non-experimental, simple descriptive study on a population of 60 uni-versity students; we carried out the data extraction process through a chatbot that applied the BarOn ICE test. After we obtained the data, the unsupervised k-means algorithm was used to group the students into sets determined based on the closest mean value obtained from the psychological test. We built a model for classifying students based on their answers to the BarOn ICE test based on K-means, with which we obtained five groups. The model classifies students by applying a dif-ferent mathematical method to that used by the models applied by psychologists.