EDUCATIONAL DATA MINING METHODS FOR TIMSS 2015 MATHEMATICS SUCCESS: TURKEY CASE

Authors : Enes FİLİZ, Ersoy ÖZ

Pages : 963-977

View : 45 | Download : 11

Publication Date : 2021-06-01

Article Type : Research Paper

Abstract :Educational data mining insert ignore into journalissuearticles values(EDM); is an important research area which has an ability of analyzing and modeling educational data. Obtained outputs from EDM help researchers and education planners understand and revise the systematic problems of current educational strategies. This study deals with an important international study, namely Trends International Mathematics and Science Study insert ignore into journalissuearticles values(TIMSS);. EDM methods are applied to last released TIMSS 2015 8th grade Turkish students` data. The study has mainly twofold: to find best performer algorithminsert ignore into journalissuearticles values(s); for classifying students’ mathematic success and to extract important features on success. The most appropriate algorithm is found as logistic regression and also support vector machines - polynomial kernel and support vector machines - Pearson VII function-based universal kernel give similar performances with logistic regression. Different feature selection methods are used in order to extract the most effective features in classification among all features in the original dataset. “Home Educational Resources”, “Student Confident in Mathematics” and “Mathematics Achievement Too Low for Estimation” are found the most important features in all feature selection methods.
Keywords : Classification algorithms, educational data mining, feature selection, mathematics success, Timss 2015

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