Abstract
This study aimed to explore the predictors of academic helplessness in middle and high school students using data from the 5th year of the Korean Children and Youth Panel Survey (KCYPS) 2018. This study applied the Lasso penalized regression among machine learning techniques. The main results are as follows. 24 variables from the middle school data and 19 variables from the high school data were selected as predictors of academic helplessness. Among the selected variables, self-study time, subjective evaluation of overall academic achievement, academic engagement, career adaptability, creative personality, happiness, self-esteem, grit, emotional problems, annual frequency of participation in youth activities, smartphone dependency, school life satisfaction, teacher relationships, and parenting attitudes have been addressed in previous studies, and the direction of the regression coefficients was consistent with previous studies. Sleep time, computer leisure time, future career decision status, desired future educational level, frequency of smartphone use by purpose (academic or work-related), and career preparation activities (career psychological and aptitude tests, career-related special lectures) were newly identified variables in this study. Based on these results, implications and suggestions for follow-up studies were presented.
Published Version
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