잠재전이분석과 머신러닝 기법을 적용한 초-중 학교급 전환에 따른 자기조절학습 변화양상과 예측변인 탐색

Abstract

The purpose of this study is to examine the transition patterns of self-regulated learning (SRL) profiles among students transitioning from elementary to middle school and to identify factors influencing these changes. To do this, the「Korea Education Longitudinal Study 2013」data was utilized. Latent transition analysis and six machine learning algorithms (Random Forest, Decision Tree, Support Vector Machine, Logistic Regression, K-Nearest Neighbors, and Artificial Neural Network) were approached for analysis. As results, first, the SRL profiles of elementary to middle school transition students were categorized into three levels: high, medium, and low. Second, Random Forest(RF) demonstrated the highest performance in classifying the transition groups of SRL patterns. Third, examining RF analysis to investigate factors influencing the transition of SRL profiles, academic achievement emerged as the most significant predictor, followed by academic self-concept, self-management, creativity, and comprehension of class materials, among others. Finally, based on the study’s results, educational implications for fostering positive development of students’ SRL were suggested.