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Predicting the Variables That Determine University (Re-)Entrance as a Career Development Using Support Vector Machines with Recursive Feature Elimination: The Case of South Korea

Author

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  • Taejung Park

    (College of Liberal Arts and Interdisciplinary Studies, Kyonggi University, 154-42 Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16227, Korea)

  • Chayoung Kim

    (College of Liberal Arts and Interdisciplinary Studies, Kyonggi University, 154-42 Gwanggyosan-ro, Yeongtong-gu, Suwon-si, Gyeonggi-do 16227, Korea)

Abstract

The current study seeks to identify variables that affect the career decision-making of high school graduates with respect to the choice of university (re-)entrance in South Korea where education has great importance as a tool for self-cultivation and social prestige. For pattern recognition, we adopted a support vector machine with recursive feature elimination (SVM-RFE) with a big-data of survey of Korean college candidates. Based on the SVM-RFE analysis results, new enrollers were mostly affected by the mesosystems of interactions with parents, while re-enrollers were affected by the macrosystems of social awareness as well as individual estimates of talent and aptitude of individual systems. By predicting the variables that affect the high school graduates’ preparation for university re-entrance, some survey questions provide information on why they make the university choice based on interactions with their parents or acquaintances. Along with these empirical results, implications for future research are also presented.

Suggested Citation

  • Taejung Park & Chayoung Kim, 2020. "Predicting the Variables That Determine University (Re-)Entrance as a Career Development Using Support Vector Machines with Recursive Feature Elimination: The Case of South Korea," Sustainability, MDPI, vol. 12(18), pages 1-11, September.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:18:p:7365-:d:410557
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    Cited by:

    1. Chayoung Kim & Taejung Park, 2022. "Predicting Determinants of Lifelong Learning Intention Using Gradient Boosting Machine (GBM) with Grid Search," Sustainability, MDPI, vol. 14(9), pages 1-13, April.

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