Intelligent System to Predict University Students Dropout

Authors

DOI:

https://doi.org/10.3991/ijoe.v18i07.30195

Keywords:

Intelligent System, Machine Learning, Prediction, University Students Dropout

Abstract


The objective of this research is to reduce the dropout rate of students in the Faculty of Systems Engineering and Informatics of the Universidad Nacional Mayor de San Marcos – FISI-UNMSM, through the implementation of an intelligent system with a data mining approach and the autonomous learning algorithm (decision trees) that predicts which students are at risk of dropping out. It was developed in Python and the free software Weka, for this purpose student data was collected from 2014 to 2020. This solution increases the availability and the level of satisfaction of the faculty; in the learning process, an accuracy percentage of 90.34% and precision of 95.91% was obtained, so the data mining model is considered valid. In addition, it was found that the variables that most influenced students in making the decision to abandon their studies were the historical weighted average, the weighted average of the last cycle and the number of credits passed.

Author Biographies

Hugo Vega, UNIVERSIDAD NACIONAL MAYOR DE SAN MARCOS

PROFESSOR AT UNIVERSIDAD NACIONAL MAYOR DE SAN MARCOS

Enzo Sanez, Universidad Nacional Mayor de San Marcos

Sistems Enginneer of systems engineering

Percy De La Cruz , Universidad Nacional Mayor de San Marcos

Professor in Universidad Nacional Mayor de San Marcos

Santiago Moquillaza, Universidad Nacional Mayor de San Marcos

Professor in Universidad Nacional Mayor de San Marcos

Johny Pretell, Universidad Autónoma del Perú

Professor in Universidad Autónoma del Perú

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Published

2022-06-14

How to Cite

Vega, H., Sanez, E., De La Cruz , P., Moquillaza, S., & Pretell, J. (2022). Intelligent System to Predict University Students Dropout. International Journal of Online and Biomedical Engineering (iJOE), 18(07), pp. 27–43. https://doi.org/10.3991/ijoe.v18i07.30195

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Section

Papers