Journal of the Institute of Convergence Signal Processing (융합신호처리학회논문지)

The Korea Institute of Convergence Signal Processing (한국융합신호처리학회)
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Implementing of a Machine Learning-based College Dropout Prediction Model

머신러닝 기반 대학생 중도탈락 예측 모델 구현 방안

  • Yoon-Jung Roh (Software Department, Tong Myeong University)
  • 노윤정 (동명대학교 소프트웨어학과)
  • Received : 2024.04.16
  • Accepted : 2024.06.30
  • Published : 2024.06.30
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Abstract

This study aims to evaluate the feasibility of an early warning system for college dropout by machine learning the main patterns that affect college student dropout and to suggest ways to implement a system that can actively prevent it. For this purpose, a performance comparison experiment was conducted using five types of machine learning-based algorithms using data from the Korean Educational Longitudinal Study, 2005, conducted by the Korea Educational Development Institute. As a result of the experiment, the identification accuracy rate of students with the intention to drop out was up to 94.0% when using Random Forest, and the recall rate of students with the intention of dropping out was up to 77.0% when using Logistic Regression. It was measured. Lastly, based on the highest prediction model, we will provide counseling and management to students who are likely to drop out, and in particular, we will apply factors showing high importance by characteristic to the counseling method model. This study seeks to implement a model using IT technology to solve the career problems faced by college students, as dropout causes great costs to universities and individuals.

본 연구는 대학생의 중도탈락에 영향을 주는 주요 패턴을 기계 학습하여 대학 중도탈락에 대한 조기 경보 시스템의 타당성을 평가하고 적극적으로 예방할 수 있는 시스템의 구현 방안을 제시하고자 한다. 이를 위해 한국교육개발원에서 실시한 한국교육종단연구 2005(Korean Educational Longitudinal Study, 2005)의 데이터를 사용하여 기계학습 기반의 5종의 알고리즘을 이용하여 성능 비교 실험을 실시하였다. 실험결과, 중도탈락 의도를 가진 학생의 식별 정확률(precosion)은 랜덤 포레스트(Random Forest)를 사용할 때 최대 94.0%, 중도탈락 의도를 가진 학생의 재현율(recall)은 Logistic Regression를 사용할 때 최대 77.0%로 측정되었다. 마지막으로 가장 높은 예측 모델을 바탕으로 중도탈락 가능성이 높은 학생을 상담 관리하며 특히, 특성별로 높은 중요도를 보이는 요인을 상담법 모델에 적용하고자 한다. 본 연구는 중도탈락이 대학과 개인에게 있어 큰 비용을 초래함과 대학생들이 직면한 진로 문제를 해결하기 위해 IT 기술을 활용한 모델을 구현하고자 한다.

Keywords

Acknowledgement

이 논문은 2022학년도 동명대학교 교내학술연구비 지원에 의하여 연구되었음(2022B005)

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