Integration of expert systems and neural networks in recognizing mathematically gifted children

Abstract

The paper investigates the possibility of integrating expert systems and neural networks in an intelligent decision support tool that will be able to assist teachers in recognizing mathematically gifted children in elementary schools. The knowledge base of the expert system consisted of five logic blocks describing basic components of a childpsilas mathematical gift identified in authorspsila previous research. The neural network model was created to learn psychological evaluations of children. The performance of individual models was compared using a 10-fold cross-validation procedure. Three neural network algorithms were tested. The results showed that the average hit rate of the expert system was higher than the average hit rate of the neural network model. In order to improve the accuracy of identifying gifted children, a postprocessing procedure was suggested that combines the results of both models. The integrated model was found to be more successful in recognizing mathematically gifted children.