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

Year 2020, Volume: 38 Issue: 2, 963 - 977, 01.06.2021

Abstract

Educational data mining (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 (TIMSS). EDM methods are applied to last released TIMSS 2015 8th grade Turkish students' data. The study has mainly twofold: to find best performer algorithm(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.

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There are 61 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Articles
Authors

Enes Filiz This is me 0000-0002-8006-9467

Ersoy Öz This is me 0000-0001-9087-434X

Publication Date June 1, 2021
Submission Date January 17, 2020
Published in Issue Year 2020 Volume: 38 Issue: 2

Cite

Vancouver Filiz E, Öz E. EDUCATIONAL DATA MINING METHODS FOR TIMSS 2015 MATHEMATICS SUCCESS: TURKEY CASE. SIGMA. 2021;38(2):963-77.

IMPORTANT NOTE: JOURNAL SUBMISSION LINK https://eds.yildiz.edu.tr/sigma/