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Analysis of Student Desertion in a Systems and Computing Engineering Undergraduate Program
Author(s) -
Luis Fernando Castro Rojas,
E. P.,
Sergio Cardona
Publication year - 2019
Publication title -
revista colombiana de computación
Language(s) - Spanish
Resource type - Journals
eISSN - 2539-2115
pISSN - 1657-2831
DOI - 10.29375/25392115.3608
Subject(s) - attrition , desertion , perspective (graphical) , context (archaeology) , computer science , action (physics) , data science , phenomenon , knowledge management , political science , artificial intelligence , medicine , dentistry , paleontology , physics , quantum mechanics , law , biology
espanolLas tecnicas de mineria de datos se enfocan principalmente en apoyar el proceso de toma de decisiones dentro de una organizacion. La desercion estudiantil es un fenomeno comun que agobia a las universidades tanto publicas como privadas, las cuales se afectan de manera social y economica. Diversos estudios se llevaron a cabo en esta area; sin embargo, por lo general se enfocan solo en los aspectos academicos, sociales, demograficos y economicos. Este articulo propone un metodo para analizar la desercion academica en el contexto de un programa de pregrado en Ingenieria de Sistemas y Computacion. Proporciona una vista de esta problematica desde la perspectiva ofrecida por KDD (descubrimiento de conocimiento en bases de datos) y usa tecnicas para descubrir patrones de comportamiento asociados con dicha problematica. A diferencia de otros trabajos similares, esta propuesta considera variables planteadas por las pruebas BADyG. Este trabajo proporcionara apoyo al proceso de toma de decisiones y fomentara la creacion de planes de accion por parte de las instituciones de educacion superior con el proposito de reducir la preocupante tasa de desercion estudiantil. EnglishData mining techniques are mainly focused on supporting the decision makers in a specific organization. Student attrition is a common phenomenon that worries public and private universities, which are affected financially and socially. Several studies have addressed this issue. However, they have mainly focused on academic, social, demographic, and economic aspects. In this paper, we propose a method for analyzing academic desertion in the context of a Systems and Computing Engineering undergraduate program by providing a view of this issue from a KDD (knowledge discovery in databases) perspective and using techniques for identifying students’ behavioral patterns. Unlike other proposals, we also consider variables provided by the BADyG test. This proposal is important because it will support higher education institutions in decision-making and creating action plans to reduce the high rate of student attrition.

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