Investigation of Mathematics-Specific Trend Variables in PISA Studies with Neural Networks and Linear Regression

Abstract

This study aimed to examine the importance levels of mathematics-specific trend variables in PISA (Programme for International Student Assessment) 2003 and 2012 in predicting mathematics performance across years with a two-step analysis method. The sample of the study was 9703 Turkish students (N2003=4855 and N2012=4848) selected by clustered and systematic sampling methods. As data analysis methods, multilayer perceptron and radial basis functions techniques of artificial neural networks and multiple linear regression were used. In the two-step analysis, first, the least erroneous model was selected as the analysis method. Then, variable importance analysis was performed with this method. The results with the lowest relative errors were obtained by the multilayer perceptron when compared to radial basis functions. The results of neural network analysis had similar or lower error rates when compared to multiple linear regression. In both PISA cycles, significant predictors were mathematics self-efficacy, mathematics interest, student-teacher relations in school, attitudes towards the school, mathematics self-concept, mathematics instrumental motivation, and teacher support in mathematics classes, respectively. The results were discussed in the light of relevant literature.