Modelo predictivo de exito académico aplicando algoritmos de aprendizaje de máquina sobre interacciones en el TEC Digital
2017
Instituto Tecnológico de Costa Rica
Proyecto de Graduación (Maestría en Ingeniería en Computación) Instituto Tecnológico de Costa Rica, Escuela de Ingeniería en Computación, 2017.
Machine learning and data mining have many applications in the educational field. In
particular, in the design of predictive models from massive data records related to learning
process. In the present work, several predictive models of academic success are designed
and evaluated, in order to create support tools for early intervention on cases of academic
failure. These Ad hoc models were designed for the TEC Digital’s educational platform.
The machine learning algorithms used are Logistic Regression, Support Vector Machines
and Neural Networks. The input dataset to the compared models consists of student’s interactions
within TEC Digital’s educational platform. The best results obtained correspond
to the algorithm of Neural Networks, but it weren’t found the parameters nor the levels of
complexity to adjust models that predict with an accuracy greater than 80 %, the students
who are most likely to fail a course.
Instituto Tecnológico de Costa Rica
Lidia Gómez
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