Predicting graduate student success: a comparison of neural networks and traditional techniques
Computers and Operations Research
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Artificial neural networks and their business applications
Information and Management
Using soft computing to build real world intelligent decision support systems in uncertain domains
Decision Support Systems - Special issue on decision support in the new millennium
Neuro-fuzzy knowledge processing in intelligent learning environments for improved student diagnosis
Information Sciences—Informatics and Computer Science: An International Journal
Adaptive and intelligent web based education system: Towards an integral architecture and framework
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
IEEE Transactions on Neural Networks
Modeling children's mathematical gift by neural networks and logistic regression
Expert Systems with Applications: An International Journal
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This paper compares the efficiency of two intelligent methods: expert systems and neural networks, in detecting children's mathematical gift at the fourth grade of elementary school. The input space for the expert system and the neural network model consisted of 60 variables describing five basic components of a child's mathematical gift identified in previous research. The expert system estimated a child's gift based on heuristically defined logic rules, while the scientifically confirmed psychological evaluation of gift based on Raven's standard progressive matrices was used at the output of neural network models. Three neural network algorithms were tested on a Croatian dataset. The results show that both the expert system and the neural network recognize more pupils as mathematically gifted than teachers do. The expert system produces the highest average hit rate, although the highest accuracy in classifying gifted children is obtained by the radial basis neural network algorithm, which also yields lower type II error. Due to the ability of expert systems to explain the result, it can be suggested that both the expert system and the neural network model have potential to serve as effective intelligent decision support tools in detecting mathematical gift in early stage, therefore enabling its further development.