Aportaciones desde la minería de datos al proceso de captación de matrícula en Instituciones de Educación Superior particulares.

Abstract

This article aims to analyze how data mining (DM) optimizes the enrollment process, with the intention of designing a predictive model to manage private enrollment for higher education institutions of Mexico. It analyzes the current status of the higher education institutions in relation to its enrollment process and the application of the DM. With a correlational method, a dataset (DS) was used to model an entropy decision tree with the help of Rapid Miner software. The results show that it is possible to build and test a predictive model management of private enrollment for higher education institutions of Mexico as the ZAM&EST model proposed by the authors.

Analysis of Factors Associated to the Enrollment and Demand of Computing-Related Careers

“…Furthermore, the proposal of Estrada-Danell et al (2016) to use data mining techniques to optimize the student recruitment process by designing a predictive model, is interesting since they consider variables such as high school average, source school segment, a percentage of scholarship assigned, and the first choice of both career and university indicated. The information to fill in the database to which they applied a decision tree algorithm, was obtained from the different promotional activities of their careers (such as visits, fairs, and advertising).…”

Diseño de un modelo para automatizar la predicción del rendimiento académico en estudiantes del IPN / Design of a model to automate the prediction of academic performance in students of IPN

“…No obstante, esta aplicación de la minería de datos es reciente en países de Latinoamérica (Estrada, Zamarripa, Zúñiga y Martínez, 2016), por lo que existen varios problemas abiertos en el uso y desarrollo de este tipo de técnicas.…”

Patrones que identifican a estudiantes universitarios desertores aplicando minería de datos educativa

“…Algunos métodos (algoritmos) de aprendizaje computacional se han aplicado para resolver problemas en este contexto, por ejemplo: se han utilizado árboles de decisión para predecir el desempeño de los estudiantes (Agaoglu, 2016;Al-Barrak y Al-Razgan, 2016;Chiheb et al, 2017;Yamao et al, 2018), medir el éxito académico (Morales y Parraga-Alava, 2018), captación de matrícula en Instituciones de Educación Superior (IES) particulares (Estrada et al, 2016) e identificación de perfiles de comportamiento (Guevara et al, 2019).…”