This study aimed to explore important predictors to career development competence and accordingly to provide foundational information for the improvement of school career education. In particular, group Mnet(a penalized regression method) was employed to consider 412 predictors of 9,546 high school students from 2018 ‘School Career Education Survey’ in one statistical model. Specifically, this study repeated 100 times of modeling with random data splitting, and obtained selection counts of each variable in the 100 repetitions. As a result, 28 variables were selected 100% of the time, comprising 24 student variables and 4 school variables. The 24 student variables selected included degrees of satisfaction towards school career education activities, learning attitudes, learning motivation, levels of recognition on career/occupation information, children-parent conversation frequencies, and career planning after graduation. The 4 school variables related to community-level support, textbook usage of the subject of “Career and Jobs”, and its semester offered, and school type. Based on the results of the study, educational intervention strategies as well as future research topics were proposed and discussed.