텍스트 분석을 통한 숙련 수요와 교과 역량 간의 매칭 분석

Abstract

The purpose of this study is to analyze the matching between job skill demands of industries and curriculum competencies provided by universities, focusing on data science majors, using textual data on online job postings and university curricula, and to provide useful implications for university curriculum program development and industry response. We collected curriculum texts and job posting texts from K-MOOCs, Job Korea, and PersonIn, derived and categorized competency and skill keywords, and calculated the cosine similarity between competencies and skills for analysis. The analysis shows that data science curriculum competencies are largely well matched to IT occupational skills and are in line with labor market occupational skill types. The “data security” and “market analysis” competencies have a high similarity to occupational skills, while the “statistical modeling” competency has a low similarity. In addition, the “database” and “manufacturing SI” skills have a high similarity to the curriculum competencies, suggesting that the university is doing a good job of meeting these skills. This study shows that analyzing the matching of competencies and skills based on textual data analysis can help universities adjust their curricula to meet the needs of the labor market, and has implications for analyzing industry skill needs, analyzing job mismatches, and linking education and industrial policies.