TY - GEN
T1 - Application of artificial neural networks for prediction of learning performances
AU - Dharmasaroja, Permphan
AU - Kingkaew, Nicha
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/10/19
Y1 - 2016/10/19
N2 - Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point averages, and composite scores of examinations during the course. Medical student learning performances were represented by their normalized T-scores of the total examination score. Three ANN models, including a support vector machine, were used to predict performance. A comparison between the models, based upon areas under the receiver operating characteristic curve values, showed no significant differences between the ANNs and logistic regression models (p > 0.05 for all pairs in the comparison). This work thus reveals the promising potential for the application of ANNs in the prediction of learning performances, in the field of medical education.
AB - Artificial neural networks (ANNs) have rarely been used in the field of medical education, especially in the prediction of learning performances. This study aims to evaluate the potential application of ANN models for predicting learning performances, in comparison with multivariate logistic regression models. The predictor variables included demographics, high-school backgrounds, first-year grade-point averages, and composite scores of examinations during the course. Medical student learning performances were represented by their normalized T-scores of the total examination score. Three ANN models, including a support vector machine, were used to predict performance. A comparison between the models, based upon areas under the receiver operating characteristic curve values, showed no significant differences between the ANNs and logistic regression models (p > 0.05 for all pairs in the comparison). This work thus reveals the promising potential for the application of ANNs in the prediction of learning performances, in the field of medical education.
KW - MLP
KW - PNN
KW - RBF
KW - SVM
UR - https://www.scopus.com/pages/publications/84997770175
U2 - 10.1109/FSKD.2016.7603268
DO - 10.1109/FSKD.2016.7603268
M3 - Conference contribution
AN - SCOPUS:84997770175
T3 - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
SP - 745
EP - 751
BT - 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
A2 - Du, Jiayi
A2 - Liu, Chubo
A2 - Li, Kenli
A2 - Wang, Lipo
A2 - Tong, Zhao
A2 - Li, Maozhen
A2 - Xiong, Ning
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
Y2 - 13 August 2016 through 15 August 2016
ER -