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izvor podataka: crosbi

Model neuronskih mreža za predviđanje matematičke darovitosti u djece (CROSBI ID 174480)

Prilog u časopisu | izvorni znanstveni rad | međunarodna recenzija

Pavleković, Margita ; Zekić-Sušac, Marijana ; Đurđević, Ivana A Neural Network Model for Predicting Children’s Mathematical Gift / Model neuronskih mreža za predviđanje matematičke darovitosti u djece // Hrvatski časopis za odgoj i obrazovanje, 13 (2011), 1; 10-41

Podaci o odgovornosti

Pavleković, Margita ; Zekić-Sušac, Marijana ; Đurđević, Ivana

hrvatski

Model neuronskih mreža za predviđanje matematičke darovitosti u djece

The paper aims to model a neural network that will be able to detect mathematically gifted pupils in the fourth grade of elementary school. The input space consisted of variables describing five basic components of a child's mathematical gift identified in previous research, while the scientifically confirmed psychological evaluation of gift based on Raven's standard progressive matrices was used at the output. Three neural network models were tested on a Croatian dataset: multilayer perceptron, radial basis, and probabilistic network. The performance of models is measured by the average hit rate obtained on the test sample. The results show that the highest accuracy is produced by a radial basis neural network, which correctly recognizes all gifted children. Such high classification accuracy shows that neural networks have potential to serve as an effective intelligent decision support tool that will be able to assist teachers in detecting mathematically gifted children, especially in schools with a lack of psychologists.

matematička darovitost ; neuronske mreže ; višeslojni perceptron ; radijalno zasnovana funkcija ; probabilistička mreža ; Ravenove progresivne matrice ; učiteljska procjena darovitosti

nije evidentirano

engleski

A Neural Network Model for Predicting Children’s Mathematical Gift

The paper aims to model a neural network that will be able to detect mathematically gifted pupils in the fourth grade of elementary school. The input space consisted of variables describing five basic components of a child's mathematical gift identified in previous research, while the scientifically confirmed psychological evaluation of gift based on Raven's standard progressive matrices was used at the output. Three neural network models were tested on a Croatian dataset: multilayer perceptron, radial basis, and probabilistic network. The performance of models is measured by the average hit rate obtained on the test sample. The results show that the highest accuracy is produced by a radial basis neural network, which correctly recognizes all gifted children. Such high classification accuracy shows that neural networks have potential to serve as an effective intelligent decision support tool that will be able to assist teachers in detecting mathematically gifted children, especially in schools with a lack of psychologists.

mathematical gift ; neural networks ; multi-layer perceptron ; radial basis function ; probabilistic network ; Raven's standard progressive matrices ; teacher estimation of gift

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nije evidentirano

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nije evidentirano

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nije evidentirano

Podaci o izdanju

13 (1)

2011.

10-41

objavljeno

1848-5189

1848-5197

Povezanost rada

Informacijske i komunikacijske znanosti, Matematika, Pedagogija

Poveznice
Indeksiranost