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TIMSS 2015 Korean Student, Teacher, and School Predictor Exploration and Identification via Random Forests

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Authors
Yoo, Jin Eun; Rho, Minjeong
Issue Date
2017-12
Publisher
서울대학교 교육종합연구원
Citation
The SNU Journal of Education Research, Vol.26 No.4, pp. 43-61
Keywords
Random forestsdecision treesmachine learninglarge-scale dataTIMSSmathematics achievement
Abstract
Previous TIMSS studies have employed conventional statistical methods, focusing on selected few indicators. The purpose of this study was to explore and identify important variables to predict students’ mathematics achievement, utilizing as many student, teacher, and school variables as possible via random forests, a popular machine learning technique. TIMSS 2015 Korean 8th graders’ student, teacher, and school datasets were merged to extract important predictors for students’ mathematics achievement. The prediction accuracy, sensitivity, and specificity of the model were 78%, 83%, and 73%, respectively. Among 413 TIMSS variables explored, variables identified as having the highest variable importance were all student variables, consistent with previous research. Scientific importance of the study was discussed as well as further research topics.
ISSN
1225-5335
Language
English
URI
http://hdl.handle.net/10371/168474
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College of Education (사범대학)Center for Educational Research (교육종합연구원)SNU Journal of Education ResearchSNU Journal of Education Research vol.26 (2017)
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