TY - GEN
T1 - Perspectives to Predict Dropout in University Students with Machine Learning
AU - Solis, Martin
AU - Moreira, Tania
AU - Gonzalez, Roberto
AU - Fernandez, Tatiana
AU - Hernandez, Maria
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/9/12
Y1 - 2018/9/12
N2 - This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.
AB - This study analyzes the performance of four machine learning algorithms with different perspectives for defining data files, in the prediction of university student desertion. The algorithms used were: Random Forest, Neural Networks, Support Vector Machines and Logistic Regression. It was found that the Random Forest algorithm with 10 variables randomly sampled as candidates in each division, was the best for predicting dropouts and that the ideal perspective for training the algorithm is to use information on all semesters that students take within a given period of time, using a classification variable that defines the non-dropout as the graduated student. In a first validation sample, this approach correctly predicted 91% of dropouts, with a sensitivity of 87%.
KW - Dropout
KW - Machine learning
KW - University students
UR - https://www.scopus.com/pages/publications/85054539090
U2 - 10.1109/IWOBI.2018.8464191
DO - 10.1109/IWOBI.2018.8464191
M3 - Contribución a la conferencia
AN - SCOPUS:85054539090
SN - 9781538675069
T3 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
BT - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Work Conference on Bioinspired Intelligence, IWOBI 2018
Y2 - 18 July 2018 through 20 July 2018
ER -