Original scientific paper

https://doi.org/10.21465/2016-SP-191-04

The application of artificial neural networks in predicting children's giftedness

Nina Pavlin-Bernardić orcid.org/0000-0002-8194-5668 ; Faculty of Humanities and Social Sciences, University of Zagreb, Zagreb, Croatia
Silvija Ravić orcid.org/0000-0001-8152-5113 ; Elementary school Sesvetska Sela, Sesvete, Croatia
Ivan Pavao Matić ; Elementary school Sesvetska Sela, Sesvete, Croatia

Abstract

Artificial neural networks have a wide use in the prediction and classification of different variables, but their application in the area of educational psychology is still relatively rare. The aim of this study was to examine the accuracy of artificial neural networks in predicting students’ general giftedness. The participants were 221 fourth
grade students from one Croatian elementary school. The input variables for artificial neural networks were teachers’ and peers’ nominations, school grades, earlier school readiness assessment and parents’ education. The output variable was the result on the Standard Progressive Matrices (Raven, 1994), according to which students were classified as gifted or non-gifted. We tested two artificial neural networks’ algorithms: multilayer perceptron and radial basis function. Within each algorithm, a number of different types of activation functions were tested. 80% of the sample was used for
training the network and the remaining 20% to test the network. For a criterion according to which students were classified as gifted if their result on the Standard Progressive Matrices was in the 95th centile or above, the best model was obtained by the hyperbolic tangent multilayer perceptron, which had a high accuracy of 100% of
correctly classified non-gifted students and 75% correctly classified gifted students in the test sample. When the criterion was the 90th centile or above, the best model was also obtained by the hyperbolic tangent multilayer perceptron, but the accuracy was lower: 94.7% in the classification of non-gifted students and 66.7% in the classification
of gifted students. The study has shown artificial neural networks’ potential in this area, which should be further explored.

Keywords

gifted students; identification of gifted students; artificial neural networks

Hrčak ID:

176765

URI

https://hrcak.srce.hr/176765

Publication date:

10.10.2016.

Article data in other languages: croatian

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