Pregled bibliografske jedinice broj: 389246
Integration of Expert Systems and Neural Networks in Recognizing Mathematically Gifted Children
Integration of Expert Systems and Neural Networks in Recognizing Mathematically Gifted Children // Proceedings of the ITI 2008 - 30th International Conference on Information Technology Interfaces / Luzar-Stiffler, Vesna ; Hljuz Dobrić, V. ; Bekić, Z. (ur.).
Zagreb: IEEE Region 8, Catalog Number CFP08498-PRT, 2008. str. 557-562 (predavanje, međunarodna recenzija, cjeloviti rad (in extenso), znanstveni)
CROSBI ID: 389246 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
Integration of Expert Systems and Neural Networks in Recognizing Mathematically Gifted Children
Autori
Pavleković, Margita ; Zekić-Sušac, Marijana ; Đurđević, Ivana
Vrsta, podvrsta i kategorija rada
Radovi u zbornicima skupova, cjeloviti rad (in extenso), znanstveni
Izvornik
Proceedings of the ITI 2008 - 30th International Conference on Information Technology Interfaces
/ Luzar-Stiffler, Vesna ; Hljuz Dobrić, V. ; Bekić, Z. - Zagreb : IEEE Region 8, Catalog Number CFP08498-PRT, 2008, 557-562
ISBN
978-953-7138-12-7
Skup
30th International Conference on Information Technology Interfaces
Mjesto i datum
Dubrovnik, Hrvatska; Cavtat, Hrvatska, 23.06.2008. - 26.06.2008
Vrsta sudjelovanja
Predavanje
Vrsta recenzije
Međunarodna recenzija
Ključne riječi
Intelligent systems in education; expert systems; neural networks; mathematical gift detection
(Intelligent systems in education; expert systems; neural networks; mathematical gift detection.)
Sažetak
The paper investigates the possibility of integrating expert systems and neural networks in an intelligent decision support tool that will be able to assist teachers in recognizing mathematically gifted children in elementary schools. The knowledge base of the expert system consisted of five logic blocks describing basic components of a child's mathematical gift identified in authors' previous research. The neural network model was created to learn psychological evaluations of children. The performance of individual models was compared using a 10-fold cross-validation procedure. Three neural network algorithms were tested. The results showed that the average hit rate of the expert system was higher than the average hit rate of the neural network model. In order to improve the accuracy of identifying gifted children, a postprocessing procedure is suggested that combines the results of both models. The integrated model was found to be more successful in recognizing mathematically gifted children.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Informacijske i komunikacijske znanosti
Napomena
Rad je citiran u bazama: INSPEC Database, Physics Abstracts, Electrical & Electronics Abstracts, Computer & Control Abstracts.
POVEZANOST RADA
Projekti:
245-0000000-3230 - Obrazovanje učenika s posebnim interesom za matematiku
Ustanove:
Ekonomski fakultet, Osijek,
Fakultet za odgojne i obrazovne znanosti, Osijek