Güre Bezek, ÖzlemKayri, MuratErdoğan, Fevzi2019-08-152019-08-152019-05-23Bezek Güre, Ö , Kayri, M , Erdoğan, F . (2019). Predicting factors affecting PISA 2015 mathematics literacy via radial basis function artificial neural network. Journal of Engineering and Technology, 3 (1), 1-11.2619-9483https://hdl.handle.net/20.500.12402/2261In this study, radial basis function artificial neural network (RBFN), which is one of the of data mining methods, was employed to determine the factors affecting PISA 2015 (Programme for International Student Assessment - PISA), Mathematics literacy. Mathematics literacy scores, which were made in categorical form with three level dependent variables, 25 independent variables, and considered to have affected the dependent variables, were employed in evaluating and validating the proposed method. Also, in order to determine factors affecting PISA 2015 Mathematics literacy, information obtained from a total of 4422 students (2165 (49%) of whom were males and 2257 (51%) of whom were females) who participated the exam was used. According to the obtained results, the correct classification rate of mathematics achievement in the radial based artificial neural network model was found to be 85.2%. In addition, it is seen that the most important factor that were affecting Mathematics literacy was Turkish language success status and the other variables that were setting significance are targeted point in school life, father education level and mother education level.eninfo:eu-repo/semantics/openAccessAttribution-ShareAlike 3.0 United StatesPISAData MiningRadial BasisArtificial Neural NetworksArticle31111