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A predictive model of school children’s types at community children center using machine learning techniques
Journal of the Korean Data & Information Science Society 2019;30:127-37
Published online January 31, 2019;
© 2019 Korean Data and Information Science Society.

Dong Su Lee1 · Jae Young Lee2 · In Hong Chang3

13Department of Computer Science and Statistics, Chosun University
2Policy Graduate School, Chosun University
Correspondence to: Professor, Department of Computer Science and Statistics, Chosun University, 309 pilmundaero, Gwangju 61452, Korea. E-mail:
Received January 4, 2019; Revised January 11, 2019; Accepted January 11, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
This study explores children ’s emotional development factors affecting the school life adaptation of children using community children center. And verify that there is a modulating effect on the type of school. The purpose of this study is to classify the emotional development factors of children using community children center by elementary school students and middle school students using the support vector machine (SVM), which is a machine learning technique. Data from this study used data from 1,905 elementary school students and 1,227 middle school students who participated in the community children center children’s panel from 2014 to 2017. The analysis tool used R (3.5.1) program. The results of this study showed that variables such as children’s emotional development directly affect school adaptation. Suggesting that it is appropriate to classify approaches to elementary and middle school students in the operation of community children centers.
Keywords : Community child center, machine learning, school adaptation, school type, support vector machine.