Published
May 18, 2023
| Pages: 613-629 | Views: 898
Abstract
The purpose of this study is to predict the mathematical literacy levels of the students participating in the research, through the data obtained from PISA 2015 exam organized by OECD using data mining and artificial neural network methods and to determine the variables that affect mathematics literacy. For this purpose, students' mathematics literacy levels and the variables that affect their mathematics literacy levels were analyzed separately for 6 different countries at different proficiency levels. The universe of the research is 519,334 students from 72 countries, who have taken PISA 2015 exam. The sample that was determined according to the purpose of the study consists of a total of 34,565 students from Singapore, Japan, Norway, USA, Turkey and the Dominican Republic, which have been observed to be at different proficiency levels. In the first stage of the study, analyzes were performed using data mining prediction methods. At this stage WEKA program was employed and M5P algorithm, which is one of the mostly used methods, was used. In the second stage of the research, the output variable was predicted from the input variables using Artificial Neural Networks methods to determine the extent to which decision trees obtained by M5P prediction method produce valid results. In the analyzes carried out in MATLAB program, the relationship between students' real math literacy scores and literacy scores predicted from input variables were examined. As a result of the study, the variables that affect mathematics literacy were found to be socio-economic status index for Singapore, Norway, United States, Turkey and Dominic. On the other hand, the variables influencing mathematics literacy for Japan were found to be mathematics learning time and father's education level. The consistency of the results was as follows: 86.10% for Singapore, 40.26% for Japan, 30.10% for Norway, 39.15% for America, 26.43% for Turkey and 29.24 % for Dominic. As a result of the study, a differentiation was found among the variables that affect mathematics literacy of the countries at different proficiency levels.
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Keywords
PISA, Mathematics literacy, Data mining, Artificial neural networks
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