TY - GEN
T1 - Prediction of graduation delay based on student performance
AU - Ojha, Tushar
AU - Heileman, Gregory L.
AU - Martinez-Ramon, Manel
AU - Slim, Ahmad
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/30
Y1 - 2017/6/30
N2 - Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines (DBMs). We train those models using actual student data. In particular, we used high school GPA, ACT score, gender and ethnicity as the main feature set for training those models. The results show that the DBMs edges out SVMs and GPs in some regards, which has been discussed in detail in the paper, although the difference in performance among the models is negligible with respect to overall accuracies obtained.
AB - Numerous factors may impact a student's ability to succeed and ultimately graduate, including pre-university preparation, as well as the student support services provided by a university. In this work we study and analyze the impact of such factors on the graduation rates of a university using three predictive models: Support Vector Machines (SVMs), Gaussian Processes (GPs) and Deep Boltzmann Machines (DBMs). We train those models using actual student data. In particular, we used high school GPA, ACT score, gender and ethnicity as the main feature set for training those models. The results show that the DBMs edges out SVMs and GPs in some regards, which has been discussed in detail in the paper, although the difference in performance among the models is negligible with respect to overall accuracies obtained.
UR - http://www.scopus.com/inward/record.url?scp=85030986050&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85030986050&partnerID=8YFLogxK
U2 - 10.1109/IJCNN.2017.7966290
DO - 10.1109/IJCNN.2017.7966290
M3 - Conference contribution
AN - SCOPUS:85030986050
T3 - Proceedings of the International Joint Conference on Neural Networks
SP - 3454
EP - 3460
BT - 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 International Joint Conference on Neural Networks, IJCNN 2017
Y2 - 14 May 2017 through 19 May 2017
ER -