Contributions to the Enrollment Process with Data Mining in Private Higher Education Institutions

Contributions to the Enrollment Process with Data Mining in Private Higher Education Institutions

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 enro...

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Bibliographic Details
Main Authors: Rafael Isaac Estrada-Danell, Roman Alberto Zamarripa-Franco, Pilar Giselle Zúñiga-Garay, Isaías Martínez-Trejo
Format: Article
Language:Spanish
Published: Universidad Nacional, Costa Rica 2016-09-01
Series:Revista Electronic@ Educare
Subjects:
Gestión educacional, planificación de la educación
administración de la educación
instituto de enseñanza superior
universidad
gestión de la información
Online Access:http://www.revistas.una.ac.cr/index.php/EDUCARE/article/view/7452
Description
Summary: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.
ISSN:1409-4258

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