Which Students Complete the Student-Designed Convergence Major?

The purpose of this study is to propose solutions aimed at activating student-designed convergence majors and improving completion rates at A University. For this, cluster analysis, an unsupervised machine learning technique, is used and determine whether the groups derived through clustering analysis effectively distinguish between students who applied to complete the student-designed convergence major and those who did not. The findings are summarized as follows: The analysis of group differences identified two central factors that distinguished the groups: 'When did you start thinking about your career?' and 'Are you confident about your major?' From these findings, the characteristics of students suited for completing the student-designed convergence major can be summarized as follows: First, the student-designed convergence major should be expected to synergize with the primary major and encourage completion among students who have established a clear career direction. Second, even if students are not confident about their major, those with a high degree of decision-making regarding their career path and a clear direction can be encouraged to complete a student-designed convergence major.