With the call for building the nation's skilled technical workforce, the demand for middle skill professionals in technical fields in Science, Technology, Engineering, and Mathematics (STEM) is increasing. The alignment of educational programming and job requirements for STEM-oriented technician sectors is essential for establishing career pathways that produce high quality middle skills professionals for technology-rich fields. Building on prior research on career pathways in Information Technologies (IT) this targeted research project investigates the alignment of educational programming, employer needs, and new employee experience in Advanced Manufacturing (AM) and tests the usefulness of tools and processes developed to assess such alignment. Focusing on the opportunities and challenges in rural areas, the overarching goal of the project is to improve rural manufacturing capacity by better understanding the relationships among curriculum, employer expectations, and student readiness for jobs in AM.
The quantitative and qualitative mixed methods research design combines content analysis and text mining with surveys, and interviews/focus groups. A review of the literature frames the study by integrating the literature on advanced manufacturing education, employment qualifications for technology-rich technician fields, and readiness to engage in entrepreneurship and intrapreneurship as employee or innovator/small business owner. The research design, instrument development, and data analyses will be guided by a theoretical approach that blends theories of human, social, and cultural capital. The research team will apply text mining approaches for text preprocessing and keyword extraction to identify learning outcomes specified in syllabi; tokenize the text; extract and identify keywords; and match national, state, and professional standards and requirements for industry certificates/certifications with course syllabi and job postings. Qualitative analysis of interviews and focus groups will examine the expectations of employers and employees through an iterative process for clustering data into themes until saturation and themes have stabilized. The extent of matching among data from different sources will be calculated and examined for congruence and divergence of educational programming and job requirements. The testing of tools/instruments and processes for conducting analyses of educational programing with job requirements in advanced manufacturing will confirm the viability of an approach for analyzing career pathways in other STEM-oriented technician programs.
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