Predicting performance from test scores using backpropagation and counterpropagation

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...

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Bibliographic Details
Published inIEEE International Conference on Neural Networks, 1994 Vol. 5; pp. 3398 - 3402 vol.5
Main Authors Fausett, L.V., Elwasif, W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1994
Subjects
Backpropagation algorithms
Calculus
Clustering algorithms
Educational institutions
History
Mathematics
Multi-layer neural network
Neural networks
Testing
Vectors
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ISBN078031901X
9780780319011
DOI10.1109/ICNN.1994.374782

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Summary: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.< >
ISBN:078031901X
9780780319011
DOI:10.1109/ICNN.1994.374782