Identifying mathematical anxiety with MLP and RBF neural networks (CROSBI ID 60664)
Prilog u knjizi | izvorni znanstveni rad | međunarodna recenzija
Podaci o odgovornosti
Đurđević Babić, Ivana ; Milić, Tomislav ; Kozić, Ana
engleski
Identifying mathematical anxiety with MLP and RBF neural networks
This research addresses the problem of mathematical anxiety which is usually associated with inadequate mathematical performance and achievement. It aims to develop neural network model for classification of students according to the degree of mathematical anxiety in order to examine and better understand the relationship and effects of physical activity along with some other factors on mathematical anxiety. For this purpose, Multilayer Perceptron (MLP) and Radial Basis Function (RBF) neural networks were used. The results of this research showed that neural network models were efficient in identifying students’ mathematical anxiety. With the purpose of exploring the relationships between the mathematical anxiety and input variables, sensitivity analysis was conducted and reported for the model with the highest overall classification accuracy.
mathematical anxiety, physical activity, neural network, MLP, RBF
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
nije evidentirano
Podaci o prilogu
250-257.
objavljeno
Podaci o knjizi
Mathematics education as a science and a profession
Kolar-Begović, Zdenka ; Kolar-Šuper, Ružica ; Jukić Matić, Ljerka
Zagreb: Element ; Fakultet za odgojne i obrazovne znanosti Sveučilišta Josipa Jurja Strossmayera u Osijeku ; Odjel za matematiku Sveučilišta Josipa Jurja Strossmayera u Osijeku
2017.
978-953-197-592-6