A prediction model based on data mining to forecast the expectations of passing from a college student

• Autores: Acosta de La Cruz, Pedro R., Flores Salinas, José A., Meza Pinto, Miguel A., Tineo Córdova, Freddy C.
• Formato: artículo
• Fecha de Publicación: 2016
• Institución: Universidad ESAN
• Repositorio: ESAN-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.esan.edu.pe:20.500.12640/3399
• Enlace del recurso: https://hdl.handle.net/20.500.12640/3399; https://doi.org/10.17577/IJERTV5IS100394
• Nivel de acceso: acceso abierto
• Materia: Artificial neural networks; Data mining; Higher education; Predictive techniques; Redes neuronales artificiales; Minería de datos; Educación superior; Técnicas predictivas; https://purl.org/pe-repo/ocde/ford#2.00.00

The present work has as objective to apply data mining techniques to develop a predictive model to forecast the chance of passing that will have a college student at the time of enrolling in a particular subject. Given that the academic record of the student can be known, and based on that information, we propose an Artificial Neural Network (ANN) that allows, using various configurations, to predict and assess our goal. The model has been applied to a compulsory subject of higher education of a University and given the results obtained. This model can be applied to any other subject analogous with satisfactory results.