수학학습 정의적 영역을 이용한 대학 이공계 전공 예측 기계학습 분석

Abstract

This study developed and analyzed models to predict the selection of STEM majors in college based on the affective domain characteristics of students during their middle and high school years, using data from the Korean Education Longitudinal Study 2005. Instrumental motivation, intrinsic motivation towards mathematics, self-efficacy in mathematics, and beliefs about intelligence were used as independent variables for prediction based on the data from the second grade of middle school and the first grade of high school. Logistic models, Multi Layer Perceptron (MLP) models, and Random Forest models were applied, and the optimal prediction model was sought by comprehensively considering various indicators. In the case of using the data from the second grade of middle school, the MLP model showed superior predictive power compared to other models. In the case of using the data from the first grade of high school, all models did not show good predictive power. The model based on the data from the second grade of middle school was deemed to have practical applicability. These results suggest that while the affective domain regarding mathematics is an important factor in the choice of a STEM major, more diverse variables and complex models are needed for predicting majors during high school years. Additionally, while recognizing the limitations of machine learning models, more theoretical discussions are needed on the relationship between major selection and the affective elements of mathematics, and it is hoped that various prediction models will be developed and used in actual practice.