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APLICACION DE ARBOLES DE CLASIFICACION A LA DETECCION PRECOZ DE ABANDONO EN LOS ESTUDIOS UNIVERSITARIOS DE ADMINISTRACION Y DIRECCION DE EMPRESAS

Authors: JOSE M. ORTIZ-LOZANO; ANTONIO RUA VIEITES; PALOMA BILBAO CALABUIG;

APLICACION DE ARBOLES DE CLASIFICACION A LA DETECCION PRECOZ DE ABANDONO EN LOS ESTUDIOS UNIVERSITARIOS DE ADMINISTRACION Y DIRECCION DE EMPRESAS

Abstract

El fenómeno de los abandonos en el nivel universitario se produce mayoritariamente en el primer curso, con una media dentro del Sistema Universitario Español del 25%. Las tasas de abandono altas son asociadas, además, con una enseñanza de baja calidad. Con el objeto de ayudar en los procesos de tutorización de estudiantes en la universidad, en el presente trabajo se analiza si es factible determinar en tres momentos: en el momento de la admisión de aquél, del inicio del curso académico y tras la realización de los primeros exámenes, un perfil del estudiante que termina presentando bajo rendimiento académico durante su primer año de estudios. Este trabajo se ha llevado a cabo mediante la aplicación de árboles de clasificación basado en los algoritmos QUEST y CART, sobre una muestra de 844 estudiantes de nuevo ingreso del Grado en Administración y Dirección de Empresas de la Universidad Pontificia Comillas. Se ha obtenido un 56% de tasa de acierto en la clasificación de estudiantes que terminan presentando bajo rendimiento académico, basada en la información disponible al finalizar el primer cuatrimestre.

Dropouts in university occur mainly in the first academic year, with an average for Spain of 25%. High dropout rates lead to prejudice against educational institutions, it harms their reputation in terms of low quality. In order to help the processes of tutoring students in the university, our work analyzes if it is feasible to get a profile of the student who is at risk of having a low academic performance in his first year in three different moments: when the admission takes place, at the beginning of the academic year, and after the first examinations. This study has used the classification tree technique based on the CART and QUEST algorithms and has used data from 844 first year students enrolled in the Business Administration Licentiate Degree at the Universidad Pontificia Comillas. We have obtained a 56% percentage of correct classified observations for those students who end up presenting low academic performance, with the information available at the end of the first semester.Keywords: Student withdrawing, Low academic performance, Classification trees, Spanish university system.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
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gold