Collaborative Research: Research: The Engineering Master’s Workforce: Leveraging Natural Language Processing Techniques to Understand Employer Demands and Student Goals

Master’s-level engineers are critical for the technology workforce as the nation seeks to advance national health, prosperity and welfare and to secure national defense. While there are four times as many engineering master’s recipients as PhDs in the United States most prior research on engineering graduate students has focused on doctoral students.

As a consequence, we know almost nothing about the experiences, motivations, career planning, and skills required by industry of master’s degree students. This project will focus on this critical segment of the workforce with an initial focus on mechanical engineering. The work will help us to systematically understand how to better prepare master’s students for their jobs so that they can make contributions in their careers from the outset.

To help inform graduate curricular offerings, we will use cutting-edge generative artificial intelligence techniques to illuminate the specific skills employers want from employees who have engineering master’s degrees. Our research will help identify potential strategies for recruiting more students to engineering master’s programs, in particular domestic students, which is a critical need for the future workforce.

The findings of this project will better inform students, employers, administrators, and those considering master’s degrees about the skills desired and expected of mechanical engineering master’s recipients.

This project will advance novel applications of natural language processing (NLP) coupled with interview research to understand the skills and benefits of terminal engineering master’s degrees. The quantitative element of the project will involve analysis of over a decade of engineering job postings. We will develop and apply an algorithm to extract skills from this substantial set of data to advance our understanding of the engineering workforce and make methodological advances in NLP.

The qualitative element will involve collection and analysis of interviews with current master’s students about their reasons for pursuing a master’s degree, including desired skills. The project will mix these qualitative and quantitative analyses to identify mis(alignments) between what is communicated from the workforce about desired skills via job advertisements and current perceptions of the workforce from current master’s students.

This research will fill an important gap in research on master’s-level engineering students, building knowledge about motivations for pursuing a master’s degree and employer expectations, including the most marketable skills. The NLP approaches developed in this project will apply to other employment sectors, disciplines, education research questions, and fields beyond engineering education research.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.