Title :
Predicting performance from test scores using backpropagation and counterpropagation
Author :
Fausett, L.V. ; Elwasif, W.
Author_Institution :
Dept. of Appl. Math., Florida Inst. of Technol., Melbourne, FL, USA
fDate :
27 Jun-2 Jul 1994
Abstract :
Two neural networks for general mapping problems, backpropagation and counterpropagation, are trained to predict students´ grades in Calculus I from placement test responses. The effect of the number of hidden units is investigated. The benefit of including topological structure on the cluster units of a counterpropagation net is illustrated. Noisy data sets are used to train the backpropagation net to improve the ability of the net to generalize
Keywords :
backpropagation; education; neural nets; Calculus I; backpropagation; cluster units; counterpropagation; neural networks; performance prediction; placement test responses; students´ grades; test scores; topological structure; Backpropagation algorithms; Calculus; Clustering algorithms; Educational institutions; History; Mathematics; Multi-layer neural network; Neural networks; Testing; Vectors;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374782
