Predicting performance from test scores using backpropagation and counterpropagation

L. V. Fausett, W. Elwasif

Research output: Contribution to conferencePaperpeer-review

36 Scopus citations

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.

Original languageEnglish
Pages3398-3402
Number of pages5
StatePublished - 1994
Externally publishedYes
EventProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7) - Orlando, FL, USA
Duration: Jun 27 1994Jun 29 1994

Conference

ConferenceProceedings of the 1994 IEEE International Conference on Neural Networks. Part 1 (of 7)
CityOrlando, FL, USA
Period06/27/9406/29/94

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