Title :
Student Achievement Prediction Based on Artificial Neural Network
Author_Institution :
Comput. & Commun. Eng. Sch., Weifang Univ., Weifang, China
Abstract :
The accurate prediction of academic students, for improving student achievement and improve teaching quality of teachers is important. This paper adopts forward neural network model, using the Levenberg-Marquardt algorithm to calculate the optimal weights of model, to achieve the mapping of impact on the behavior of normal university students academic. Experimental samples based on 800 students, calculated that up to 79.1% accuracy of the model, the proposed method is effective.
Keywords :
computer aided instruction; further education; neural nets; teaching; Levenberg-Marquardt algorithm; academic students; artificial neural network; forward neural network model; student achievement prediction; student behavior; teachers; teaching quality; university students; Accuracy; Biological neural networks; Convergence; Educational institutions; Neurons; Prediction algorithms; Training; Levenberg-Marquardt algorithm; achievement prediction; neural network; optimal weight; sample;
Conference_Titel :
Internet Computing & Information Services (ICICIS), 2011 International Conference on
Conference_Location :
Hong Kong
Print_ISBN :
978-1-4577-1561-7
DOI :
10.1109/ICICIS.2011.126
