Predicting graduate student success: a comparison of neural networks and traditional techniques
Computers and Operations Research
Artificial neural networks and their business applications
Information and Management
Advanced algorithms for neural networks: a C++ sourcebook
Advanced algorithms for neural networks: a C++ sourcebook
Model selection in neural networks
Neural Networks
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
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
Expert Systems with Applications: An International Journal
Comparison of intelligent systems in detecting a child's mathematical gift
Computers & Education
IEEE Transactions on Neural Networks
Hi-index | 12.05 |
The purpose of the paper was to extract important features of children's mathematical gift by using neural networks and logistic regression, in order to create a model that will assist teachers in elementary schools to recognize mathematically gifted children in an early stage, therefore enabling further development and realization of that gift. The initial model was created on the basis of a theoretical background and heuristical knowledge on giftedness in mathematics, including five components: (1) mathematical competencies, (2) cognitive components of gift, (3) personal components that contribute gift development, (4) environmental factors, and (5) efficiency of active learning and exercising methods, as well as grades and out-of-school activities of pupils in the fourth year of elementary school. The three neural network classification algorithms were tested in order to extract the important variables for detecting mathematically gifted children. The best neural network model was selected on the basis of a 10-fold cross-validation procedure. The model was also investigated by the logistic regression. Important predictors detected by two methods were compared and analyzed. The results show that both methods extract similar set of variables as the most important, including grades in mathematics, mathematical competencies of a child regarding numbers and calculating, but also grades in the literature, and environmental factors.