Classification of Students’ Mathematical Literacy Score Using Educational Data Mining: PISA 2015 Turkey Application

Cumhuriyet Science Journal

article

Hasan Aykut KARABOĞA¹  Serkan AKOGUL²  İbrahim DEMİR³ 

[1] AMASYA ÜNİVERSİTESİ;PAMUKKALE UNIVERSITY;Turkish Statistical Institute

关键词: Classification;    Educational data mining;    PISA 2015;    Mathematics education;    Discriminant analysis.;   

DOI  :  10.17776/csj.1136733

学科分类：工程和技术（综合）

来源: Cumhuriyet University

【 摘 要 】

PISA 2015 mathematical literacy score of Turkey is 420 while the average score of all countries is 461. It is understood that; Turkish students’ PISA 2015 mathematical literacy score was lower than the average. The basic reasons for the below average score need to be truly examined and developmental activities should be revealed. The aim of this study is to classify students according to the factors affecting their mathematical literacy score and to reveal the effects of these factors in classification.The data of the study is obtained from 5895 students who participated in PISA 2015. In this study, we used Random Forest, Naïve Bayes Classifier, Logistic Regression, Decision Tree Algorithm and Discriminant Analysis as classifiers. According to the results, Random Forest method produced more accurate scores than other methods with 76.32% accuracy. We also calculated the correct classification rate and determined the factors that positively and negatively affect the classification with discriminant analysis. According to the discriminant analysis home possessions, information and computer technology resources at home and students' expected occupational status were the most positive effective variables on mathematical literacy score. On the other hand, family wealth possessions, student-related factors affecting school climate and anxiety have negative effect on mathematical literacy score.

【 授权许可】

CC BY-NC-ND