Data mining to identify risk factors associated with university students dropout

. This paper presents the identification of university students dropout patterns by means of data mining techniques. The database consists of a series of questionnaires and interviews to students from several universities in Colombia. The information was processed by the Weka software following the Knowledge Extraction Process methodology with the purpose of facilitating the interpretation of results and finding useful knowledge about the students. The partial results of data mining processing on the information about the generations of students of Industrial Engineering from 2016 to 2018 are analyzed and discussed, finding relationships between family, economic, and academic issues that indicate a probable desertion risk in students with common behaviors. These relationships provide enough and appropriate information for the decision-making process in the treatment of university dropout.

• Autores: Viloria Silva, Amelec Jesus; Castro Sarmiento, Alex; María Santodomingo, Nicolás; María Santodomingo, Nicolas Elias; Márquez Blanco, Norka; Cadavid Basto, Wilmer; Hernández P, Hugo; Navarro Beltrán, Jorge; de la Hoz Hernández, Juan; Romero, Ligia
• Tipo de recurso: http://purl.org/coar/resource_type/c_816b
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/5225
• Acceso en línea: https://hdl.handle.net/11323/5225; https://repositorio.cuc.edu.co/
• Palabra clave: Knowledge extraction process; Tutoring; Decision making; Data mining
• Rights: openAccess
• License: Attribution-NonCommercial-ShareAlike 4.0 International