Integration of data technology for analyzing university dropout

Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the data provided by the Engineering departments of the University of Mumbai, in India, determine that the variables that best explain a student's dropout are the socioeconomic factors and the income score provided by the University Admission Test (UAT). According to the decision tree technique, it is concluded that the retention is 78.3%. The quality of the classifiers allows to ensure that their predictions are correct, with statistical levels of ROC curve are 76%, 75%, and 83% successful for Bayesian network classifiers, decision tree, and neural network respectively.

• Autores: amelec, viloria; Garcia Padilla, Jholman; Vargas Mercado, Carlos; Hernández Palma, Hugo; ORELLANO LLINAS, NATALY; ARRAZOLA DAVID, MONICA JUDITH
• Tipo de recurso: Article of journal
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/5878
• Acceso en línea: https://hdl.handle.net/11323/5878; https://repositorio.cuc.edu.co/
• Palabra clave: University retention; University dropout; Data mining; Education; Engineering; Big Data
• Rights: openAccess
• License: CC0 1.0 Universal