초록 열기/닫기 버튼
본 연구의 목적은 서울시 중학생의 진로성숙도 예측 요인을 찾기 위해 머신러닝 기법(Decision Tree, Random Forest, XGBoost)을 서울교육종단연구 4∼6차년도 데이터에 적용하였다. 적용에 따라 도출된 세 가지 머신러닝 모형의 변수 중요도와 각 지표별 성능을 확인하였다. 또한 XGBoostExplainer 패키지를 활용하여 모형을 해석하였으며, 데이터 전처리와 분석 모두 R과 R Studio를 활용하였다. 그 결과 각 모형별로 변수중요도 순위는 다소 차이가 있으나 ‘성취목표’, ‘창의성’, ‘자아개념’, ‘부모자녀와의 관계’, ‘회복탄력성’이높은 순위를 보였다. 또한 XGBoostExplainer를 활용하여 패널별 진로성숙도에 정적⋅부적 영향을 주는 요인을 탐색하였고, ‘성취목표’가 진로성숙도 예측 최우선 요인임을 찾을 수 있었다. 본 연구결과를 바탕으로 머신러닝 및 변수선택 방법의 비교연구와 서울교육종단연구 코호트별 비교연구가 수행되어야 함을제언하였다.
The purpose of this study was to apply machine learning techniques (Decision Tree, Random Forest, XGBoost) to data from the 4th∼6th year of the Seoul Education Longitudinal Study to find the factors predicting the career maturity of middle school students in Seoul city. In order to evaluate the machine learning application result, the performance of the model according to the indicators was checked. In addition, the model was analyzed using the XGBoostExplainer package, and R and R Studio tools were used for this study. As a result, there was a slight difference in the ranking of variable importance by each model, but the rankings were high in ‘Achievement goal awareness’, ‘Creativity’, ‘Self-concept’, ‘Relationship with parents and children’, and ‘Resilience’. In addition, using the XGBoostExplainer package, it was found that the factors that protect and deteriorate career maturity by panel and ‘Achievement goal awareness’ is the top priority factor for predicting career maturity. Based on the results of this study, it was suggested that a comparative study of machine learning and variable selection methods and a comparative study of each cohort of the Seoul Education Termination Study should be conducted.
키워드열기/닫기 버튼
Middle school students, Career maturity, Machine learning, Seoul Education Longitudinal Study, SELS
