Análisis del nivel de rendimiento académico de estudiantes de los primeros ciclos utilizando arboles de decisión como técnica de inteligencia artificial, para determinar las posibles causas del bajo rendimiento en la facultad de ciencias de la ingeniería y aplicadas de la universidad técnica de Cotopaxi.
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Date
2019-08
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Ecuador: Latacunga: Universidad Técnica de Cotopaxi (UTC).
Abstract
Description
This research project tries to determine the possible causes of poor academic performance
through the application of data mining, using decision trees as an artificial intelligence
technique to predict their academic performance through factors that affect the students in
their development. The results obtained with the application of decision trees to 236 students
through a survey were able to verify the factors that influenced the students' performance
according to their average, the academic variables obtained more significant information gain
since they have higher accuracy for prediction, 87.33% of the students are very well in their
level of academic performance. For the analysis of the information obtained on the attitudinal,
academic, and identification factors, the Rapidminer tree, set role decision tools that are data
mining software was applied, the use of the variables together corresponding to a single
aspect that has no greater accuracy than the combination of variables of different aspects.
Keywords
ANÁLISIS DEL NIVEL DE RENDIMIENTO, ARBOLES DE DECISIÓN, INTELIGENCIA ARTIFICIAL
Citation
Tomalo Morales Shirley Vanessa (2019); Análisis del nivel de rendimiento académico de estudiantes de los primeros ciclos utilizando arboles de decisión como técnica de inteligencia artificial, para determinar las posibles causas del bajo rendimiento en la facultad de ciencias de la ingeniería y aplicadas de la universidad técnica de Cotopaxi. UTC. Latacunga. 81 p.