WSEAS Transactions on Information Science and Applications
Print ISSN: 1790-0832, E-ISSN: 2224-3402
Volume 10, 2013
A Comparative Study of Hierarchical ANFIS and ANN in Predicting Student Academic Performance
Authors: ,
Abstract: The accurate prediction of student academic performance is of importance to institutions as it provides valuable information for decision making in the admission process and enhances educational services by allocating customized assistance according to the predictions. The purpose of this study is to investigate the predictive ability of two models: the hierarchical ANFIS and ANN. We used previous exam results and other factors, such as the location of the student’s high school and the student’s gender, as input variables, and predicted the student’s expected performance. The simulation results of the two models were then discussed and analyzed. It was found that the hierarchical ANFIS model outperformed the ANN model in the prediction of student academic performance. These results show the potential of the hierarchical ANFIS model as a predictor. It is expected that this work may be used to assist in student admission procedures and strengthen the service system in educational institutions.
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Keywords: Adaptive Neuro-Fuzzy Inference System, Artificial neural network, Prediction, Student academic performance, Higher education