Analysis of data mining techniques for constructing a predictive model for academic performance

This paper presents and analyzes the experience of applying certain data mining methods and techniques on 932 Systems Engineering students data, from El Bosque University in Bogotá, Colombia; effort which has been pursued in order to construct a predictive model for students academic performance. Previous works were reviewed, related with predictive model construction within academic environments using decision trees, artificial neural networks and other classification techniques. As an iterative discovery and learning process, the experience is analyzed according to the results obtained in each of the process iterations. Each obtained result is evaluated regarding the results that are expected, the datas input and output characterization, what theory dictates and the pertinence of the model obtained in terms of prediction accuracy. Said pertinence is evaluated taking into account particular details about the population studied, and the specific needs manifested by the institution, such as the accompaniment of students along their learning process, and the taking of timely decisions in order to prevent academic risk and desertion. Lastly, some recommendations and thoughts are laid out for the future development of this work, and for other researchers working on similar studies.

• Autores: Merchán Rubiano, Sandra Milena; Duarte Garcia, Jorge Alberto
• Tipo de recurso: Article of journal
• Fecha de publicación: 2020
• Institución: Universidad El Bosque
• Repositorio: Repositorio U. El Bosque
• Idioma: eng
• OAI Identifier: oai:repositorio.unbosque.edu.co:20.500.12495/3527
• Acceso en línea: http://hdl.handle.net/20.500.12495/3527; https://doi.org/10.1109/TLA.2016.7555255; https://repositorio.unbosque.edu.co
• Palabra clave: Courseware; Education; Artificial intelligence; Data mining; Predictive modeling; Academic risk prevention
• Rights: openAccess
• License: Acceso abierto