Hlm ve Ysa Yöntemlerinin Pısa 2018 Okuduğunu Anlama Becerilerini Yordama Düzeylerinin İncelenmesi
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In this research, it is aimed to compare hierarchical linear modeling and artificial neural network estimation methods in predicting students' reading literacy in the Program for International Student Assessment (PISA) 2018 application. In accordance with this purpose; It is planned to determine how students' PISA reading literacy scores are estimated at the individual and school level, and the explained variance and error values of the artificial neural network and hierarchical linear modeling used in estimation. The type of study is, in a way, relational research because of the establishment of models in which there are relationships between dependent and independent variables. On the other hand, it is a study in which analyzes are carried out with two methods for each country sampled in the study and the results obtained are compared in terms of the explained variance and error values. In this research, findings about how artificial neural networks (ANN) which is a data mining method that has just started to be used in the field of education, and hierarchical linear modeling (HLM) perform. It has been determined that HLM carries out the estimation process with lower error and higher R^2 in the data set used in the analysis of multi-level data compared to ANN. In addition, HLM provides more information about the predictive level of the variables and the variance that is not explained by the variables in the model compared to ANN. For this reason, HLM analysis was used to examine the variables that affect reading literacy in the study. As a result, it was seen that the individual level and school level variables added to the model had a statistically significant effect on reading comprehension achievement. While lack of educational material at school cause negative effects on reading literacy, it has been determined that economic-social-cultural situation, metacognitive strategies, disciplinary climate in the classroom, teacher support, teacher-directed instruction, and staff shortage variables have positive effects. The results obtained are generally in agreement with similar studies in the literature.