Salt Lake City, Utah
June 23, 2018
June 23, 2018
July 27, 2018
Recent research exists that utilizes machine learning techniques to analyze the underlying patterns in the job market. In this paper, Skill Miner System (SMS) is presented. SMS utilizes text mining algorithms to identify these skills and qualifications employers seek for in STEM fields. In addition, SMS generates a skill demand index (SDI), which is used to determine the demand for particular skills. This study focuses on Industrial Engineering but can be easily adaptable to other fields. Data for this study was collected by scrapping various job postings for Industrial Engineers. The data used to develop SMS consisted of more than 5,000 jobs. The underlying pattern of the job market is compared to a public database of various occupational information, O*NET. O*NET, sponsored by the U.S. Department of Labor, is one of the most comprehensive publicly accessible databases of occupational requirements for skills, abilities and knowledge. However, by itself the information in O*NET is not enough to characterize the distribution of occupations required in a given market or region. SMS is different from O*NET as it detects skills required in the job market on a more frequent basis and provides a metric to determine the importance of a skill. This paper shows that SMS is able to detect skills that are required by the job market and are not mentioned in O*NET. SMS sets a weight to portray the demand for each skill. This is beneficial for institutions and organizations to remain competitive in the job market. At the University of Illinois at Chicago, senior engineering undergraduates in the Department of Mechanical and Industrial Engineering are required to take a professional development course (PDC) to assist in their career development. PDC employs SMS routinely to help each student cater to various job positions. In addition, resumes are improved by using additional relevant keywords employers seek, which are detected by SMS. SMS has assisted in increasing the number of students that graduate with a job offers and in the course's goal of helping students obtain careers. The analysis presented in this paper shows that SMS can benefit various stakeholders, such as universities, students, employers, and recruiting firms. Universities will have a better understanding of the job market and will be able to improve the education of their students with the evolving job market. Students will be more qualified and better prepared for the job market. Employers and recruiting firms will be able to remain competitive in the job market.
Darabi, H., & Karim, F. S. M., & Harford, S. T., & Douzali, E., & Nelson, P. C. (2018, June), Detecting Current Job Market Skills and Requirements Through Text Mining Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30284
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