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

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.

Impact of the COVID-19 Pandemic on the Revenue of the Catering Industry: Taiwan as an Example

“…To further elaborate the effectiveness of the proposed methodology, several existing methods including multiple linear regression (MLR) (Su, 2020; Yang et al, 2020), RF (Tanizaki et al, 2021), RFE+RF (Park & Kim, 2020; Ustebay et al, 2018) DNN (Sun et al, 2021), and RFE+DNN (Abedinia et al, 2017; Darst et al, 2018; Lin et al, 2019; Suganya & Shanthi, 2012; Wang & Chen, 2019) were also applied to the collected data for comparison. The configuration of each DNN has been optimized.…”

“…In the fuzzy big data analytics approach, two of the most widely used feature selection techniques, the RF method with RFE (Abedinia et al, 2017; Darst et al, 2018; Park & Kim, 2020; Ustebay et al, 2018) and correlation analysis (Gogtay & Thatte, 2017) are jointly applied. The former is a post-selection method that selects features to optimize prediction performance, while the latter is a pre-selection method that selects features based on their values.…”

“…There are many existing methods, including variance threshold (i.e., removing features with low variance), univariate feature selection (i.e., correlation analysis) (Gogtay & Thatte, 2017), the RFE method (Yan & Zhang, 2015), L1-based feature selection (Shekar & Dagnew, 2020), tree-based feature selection (Suresh & Bharathi, 2016), sequential feature selection (Aggrawal & Pal, 2020), etc. Among them, the RFE method has received the most attention in big data related applications (Park & Kim, 2020; Ustebay et al, 2018; Yan & Zhang, 2015).…”

Predicting the percentage of student placement: A comparative study of machine learning algorithms

“…University selection is typically a one-off choice that leads to an extended consumption experience of four to five years, and the decision will affect the career of the "consumers" over a longer period (Moogan et al, 1999;Walsh et al, 2015). Park and Kim (2020) have recently identified the factors affecting high school graduates' career decision-making on their choice of university (re-) entrance in South Korea. Based on the findings of the study, new enrollees were primarily affected by the relationship between parents and re-enrollees, while social consciousness macrosystems, as well as human skill and capacity estimates of individual structures, have affected re-enrollees.…”