Detection of the success patterns in the university studies of the Universidad Continental
Abstract
The objective of the study was to detect the success patterns in the university studies of Universidad Continental students from the academic and sociodemographic information collected in the first cycle of studies. The research had a cross-sectional descriptive design, the students were from diverse professional careers that developed their students between 2012 and 2017. For the automated extraction of knowledge the methodology of data mining was used (CRISPDM) and the Clementine software, throught the multilayer perceptron neural network, it was possible to identify the variables that most impact the success and abandonment of university studies, To obtain the decision tree,algorithm C5.0 was applied and for grouping (three groups) the algorithms k-means and TwoStep, and these results were compared using a confusion matrix. The results show that the students that abandoned their university studies did not pass the fifth cycle. The variables that most influence the success of the university studies are: student`s civil status, amount of the pension, marital status of the parents and with whom the student lives in Huancayo. On the other hand, the variables that most influence the abandonment of the studies are , whit whom the student lives, the student marital status, satisfaction with the teacher’s performance and the parent`s marital status. In conclusion, the socio demographic variables are those that have the most predominance over the success and failure of university studies.
References
Chapman, P., Clinton, P., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., & Wirth, R. CRISP-DM 1.0. Step-by-step data mining guide (2000). [Software de computación]. Clementine SPPS (versión 12,0). SPSS Inc.
CINDA. (2006). Repitencia y deserción universitaria en America Latina Colección Gestión Universitaria, L. González (Ed.).
Hernández Orallo, J., Ramírez Quintana, J., & Ferri Ramírez, C. (2008). Introducción a la Minería de Datos. Madrid: Pearson Prentice Hall.
Mariscal G, Marbán Ó, Fernández C. A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review.
Ocaranza, O. and M. Quiroz (2005). Deserción estudialtil en el pregrado en la Pontificai Universidad Católica de Valparaíso, Chile. Chile.
Perez López, C., & Santin Gonzalez, D. (2006). Data Mining - Soluciones Con Enterprise Miner Con 1 CD: Alfaomega Grupo Editor.
Spositto, O., Etcheverry, M., Ryckeboer, H., & Bossero, J. (2009). Aplicación de técnicas de minería de datos para la evaluación del rendimiento académico y la deserción estudiantil.
Timarán Pereira, R. (2009). Detección de Patrones de Bajo Rendimiento Académico y Deserción Estudiantil con Técnicas de Minería de Datos. Paper presentado en la Octava Conferencia Iberoamericana en Sistemas, Cibernética e Informática: CISCI 2009, Orlando, Florida ~ EE.UU.
