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Pregled bibliografske jedinice broj: 832060

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


Pavlin-Bernardić, Nina; Ravić, Silvija; Matić, Ivan Pavao
The application of artificial neural networks in predicting children's giftedness // Suvremena psihologija, 19 (2016), 1; 49-60 doi:10.21465/2016-SP-191-04 (međunarodna recenzija, članak, znanstveni)


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

Autori
Pavlin-Bernardić, Nina ; Ravić, Silvija ; Matić, Ivan Pavao

Izvornik
Suvremena psihologija (1331-9264) 19 (2016), 1; 49-60

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
gifted students ; identification of gifted students ; artificial neural networks

Sažetak
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 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% was used to test the network. For a criterion according to which students were classified as gifted if their result on Standard progressive matrices was in 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 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 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.

Izvorni jezik
Engleski

Znanstvena područja
Psihologija



POVEZANOST RADA


Ustanove:
Filozofski fakultet, Zagreb,
Fakultet hrvatskih studija, Zagreb

Profili:

Avatar Url Nina Pavlin-Bernardić (autor)

Poveznice na cjeloviti tekst rada:

doi hrcak.srce.hr

Citiraj ovu publikaciju:

Pavlin-Bernardić, Nina; Ravić, Silvija; Matić, Ivan Pavao
The application of artificial neural networks in predicting children's giftedness // Suvremena psihologija, 19 (2016), 1; 49-60 doi:10.21465/2016-SP-191-04 (međunarodna recenzija, članak, znanstveni)
Pavlin-Bernardić, N., Ravić, S. & Matić, I. (2016) The application of artificial neural networks in predicting children's giftedness. Suvremena psihologija, 19 (1), 49-60 doi:10.21465/2016-SP-191-04.
@article{article, year = {2016}, pages = {49-60}, DOI = {10.21465/2016-SP-191-04}, keywords = {gifted students, identification of gifted students, artificial neural networks}, journal = {Suvremena psihologija}, doi = {10.21465/2016-SP-191-04}, volume = {19}, number = {1}, issn = {1331-9264}, title = {The application of artificial neural networks in predicting children's giftedness}, keyword = {gifted students, identification of gifted students, artificial neural networks} }
@article{article, year = {2016}, pages = {49-60}, DOI = {10.21465/2016-SP-191-04}, keywords = {gifted students, identification of gifted students, artificial neural networks}, journal = {Suvremena psihologija}, doi = {10.21465/2016-SP-191-04}, volume = {19}, number = {1}, issn = {1331-9264}, title = {The application of artificial neural networks in predicting children's giftedness}, keyword = {gifted students, identification of gifted students, artificial neural networks} }

Časopis indeksira:


  • Scopus


Uključenost u ostale bibliografske baze podataka::


  • PsychINFO
  • Social Sciences Citation Index


Citati:





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