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.
Highlights
Large-scale assessments are gaining more and more importance in terms of comparing the education systems of countries in the globalizing world
Many studies are organized by the Organisation for Economic Co-operation and Development (OECD), which are applied worldwide and affect education policies in many countries. Some of these applications are Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), The Teaching and Learning International Survey (TALIS), and Programme for International Student Assessment (PISA) in which thousands of students are involved from many countries around the world
The aim of this study is to examine the importance levels of mathematics-specific trend domain variables in predicting PISA 2003 and 2012 mathematics performances of the students with artificial neural networks and multiple linear regression
Summary
Large-scale assessments are gaining more and more importance in terms of comparing the education systems of countries in the globalizing world To this end, many studies are organized by the Organisation for Economic Co-operation and Development (OECD), which are applied worldwide and affect education policies in many countries. Many studies are organized by the Organisation for Economic Co-operation and Development (OECD), which are applied worldwide and affect education policies in many countries Some of these applications are Trends in International Mathematics and Science Study (TIMSS), Progress in International Reading Literacy Study (PIRLS), The Teaching and Learning International Survey (TALIS), and Programme for International Student Assessment (PISA) in which thousands of students are involved from many countries around the world. The common purpose of these studies is to compare the potential of each country by considering its own conditions, to analyze education policies, and to make some inferences in this context
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