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Detecting Current Job Market Skills and Requirements Through Text Mining

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Conference

2018 ASEE Annual Conference & Exposition

Location

Salt Lake City, Utah

Publication Date

June 23, 2018

Start Date

June 23, 2018

End Date

July 27, 2018

Conference Session

IED Technical Session: Preparing Programs for the Future

Tagged Division

Industrial Engineering

Page Count

16

Permanent URL

https://peer.asee.org/30284

Download Count

120

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Paper Authors

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Houshang Darabi University of Illinois, Chicago Orcid 16x16 orcid.org/0000-0001-7881-6542

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Dr. Houshang Darabi is an Associate Professor of Industrial and Systems Engineering in the Department of Mechanical and Industrial Engineering (MIE) at the University of Illinois at Chicago (UIC). Dr. Darabi has been the Director of Undergraduate Studies in the Department of MIE since 2007. He has also served on the College of Engineering (COE) Educational Policy Committee since 2007. Dr. Darabi is the recipient of multiple teaching and advising awards including the UIC Award for Excellence in Teaching (2017), COE Excellence in Teaching Award (2008, 2014), UIC Teaching Recognitions Award (2011), and the COE Best Advisor Award (2009, 2010, 2013). Dr. Darabi has been the Technical Chair for the UIC Annual Engineering Expo for the past 7 years. The Annual Engineering Expo is a COE’s flagship event where all senior students showcase their Design projects and products. More than 700 participants from public, industry and academia attend this event annually.
Dr. Darabi is an ABET IDEAL Scholar and has led the MIE Department ABET team in two successful accreditations (2008 and 2014) of Mechanical Engineering and Industrial Engineering programs. Dr. Darabi has been the lead developer of several educational software systems as well as the author of multiple educational reports and papers. Dr. Darabi’s research group uses Big Data, process mining, data mining, Operations Research, high performance computing, and visualization techniques to achieve its research and educational goals. Dr. Darabi’s research has been funded by multiple federal and corporate sponsors including the National Science Foundation, and National Institute of Standards and Technology.

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Fazle Shahnawaz Muhibul Karim University of Illinois, Chicago

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Fazle Karim is an aspiring data scientist who is completing his PhD in the Mechanical and Industrial Engineering department at University of Illinois at Chicago. He received his BSIE in 2012 from the University of Illinois at Urbana Champaign. He is currently the lead data scientist at PROMINENT lab, the leading process mining research facility at the university. He has taught courses in Probability & Statistic in Engineering, Work Productivity Analysis, Quality Control & Reliability, and Safety Engineering. His research interest includes education data mining, health care data mining, and time series analysis.

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Samuel Thomas Harford

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Samuel Harford is a graduate research assistant at the University of Illinois at Chicago's Mechanical and Industrial Engineering Department. He received his BSIE in May 2016 from UIC and is currently pursuing his MS. Since 2015 he has done multiple projects in education data mining, some in collaboration with the Dean of Engineering. His research interests include healthcare and education data mining.

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Elnaz Douzali University of Illinois, Chicago

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Peter C. Nelson University of Illinois, Chicago

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Peter Nelson was appointed Dean of the University of Illinois at Chicago’s (UIC) College of Engineering in 2008. Prior to assuming his deanship, Professor Nelson was head of the UIC Department of Computer Science. In 1991, Professor Nelson founded UIC's Artificial Intelligence Laboratory, which specializes in applied intelligence systems projects in fields such as transportation, manufacturing, bioinformatics and e-mail spam countermeasures. Professor Nelson has published over 80 scientific peer reviewed papers and has been the principal investigator on over $40 million in research grants and contracts on issues of importance such as computer-enhanced transportation systems, manufacturing, design optimization and bioinformatics. These projects have been funded by organizations such as the National Institutes of Health, the National Science Foundation, the National Academy of Sciences, the U.S. Department of Transportation and Motorola. In 1994-95, his laboratory, sponsored by the Illinois Department of Transportation, developed the first real-time traffic congestion map on the World Wide Web, which now receives over 100 million hits per year. Professor Nelson is also currently serving as principal dean for the UIC Innovation Center, a collaborative effort between the UIC Colleges of Architecture, Design and the Arts; Business Administration; Medicine and Engineering.

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Abstract

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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