Towards an Automatic Approach for Assessing Program Competencies

Skills analysis is an interdisciplinary area that studies labor market trends and provides recommendations for developing educational standards and re-skilling efforts. We leverage techniques in this area to develop a scalable approach that identifies and evaluates educational competencies. In this work, we developed a skills extraction algorithm that uses natural language processing and machine learning techniques. We evaluated our algorithm on a labeled dataset and found its performance to be competitive with state-of-the-art methods. Using this algorithm, we analyzed student skills, university course syllabi, and online job postings. Our cross-sector analysis provides an initial landscape of skill needs for specific job titles. Additionally, we conducted a within-sector analysis based on programming jobs, computer science curriculum, and undergraduate students. Our findings suggest that students have a variety of hard skills and soft skills, but they are not necessarily the ones that employers want. The data also suggests these courses teach skills that are somewhat different from industry needs, and there is a lack of emphasis on soft skills. These results provide an initial assessment of the program competencies for a computer science program. Future work includes more data gathering, improving the algorithm, and applying our method to assess additional educational programs.