Lasso 회귀모형과 랜덤 포레스트 분석 기법을 활용한 대학 신입생의 학업 부진 영향요인 분석

Abstract

Objectives This study aims to provide implications for university education policy research for the academic adjustment of freshmen by comprehensively analyzing the factors that affect academic underachievement of freshmen. Methods For this purpose, we used data from a total of 8,516 college freshmen who responded to the fourth-year survey conducted by the Korean Employment Panel, and applied machine learning techniques, namely Lasso regression analysis and Random Forest analysis. Results As a result of Lasso regression analysis, it was found that gender, implementation of the plan, and provision of company recruitment information were the variables that affected academic underachievement of college freshmen. As a result of random forest analysis, personal life culture, family background, and school environment were identified as major factors. As a result of examining the newly derived variables, the ability to use digital technology, learning habits and motivation, and emotional stability were confirmed. By comprehensively considering these variables, a more accurate understanding of academic underachievement can be sought. Conclusions The factors influencing academic underachievement of college freshmen are more complex and diverse. Individualized support and innovation in university education suitable for the digital native generation will be needed to prevent academic underachievement.