The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Design of the Management System for Students at Risk of Dropout using Machine Learning

머신러닝을 이용한 학업중단 위기학생 관리시스템의 설계

  • 반재훈 (고신대학교 IT경영학과) ;
  • 김동현 (동서대학교 컴퓨터공학부) ;
  • 하종수 (경남정보대학교 방송영상과)
  • Received : 2021.10.22
  • Accepted : 2021.12.17
  • Published : 2021.12.31
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Abstract

The proportion of students dropping out of universities is increasing year by year, and they are trying to identify risk factors and eliminate them in advance to prevent dropouts. However, there is a problem in the management of students at risk of dropping out and the forecast is inaccurate because crisis students are managed through the univariable analysis of specific risk factors. In this paper, we identify risk factors for university dropout and analyze multivariables through machine learning method to predict university dropout. In addition, we derive the optimization method by evaluation performance for various prediction methods and evaluate the correlation and contribution between risk factors that cause university dropout.

학업을 중단하는 학생들의 비율이 해마다 증가하고 있어 대학은 학업중단을 막기 위하여 위험요소를 파악하고 이를 사전에 제거하기 위해 노력하고 있다. 그러나 특정 위험요소의 단변수 분석을 통해 위기학생을 관리하고 있어 예측이 부정확한 문제가 발생하고 있다. 본 연구에서는 이러한 문제점을 해결하기 위하여 학업중단 위험요소를 파악하고 학업중단 예측을 위해 머신러닝 방법을 통해 다변수 분석을 실시한다. 또한 다양한 예측방법별로 성능평가를 수행하여 최적화 방법을 도출하고 학업중단을 발생시키는 위험요소간의 연관성과 기여도를 평가한다.

Keywords

References

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