Triangulating Competencies: A Deductive Approach to Facilitating Advanced Manufacturing Technicians' Readiness in the Rural Economy

• In Event: AERA Roundtable Session 8
  In Roundtable Session: "Learning by Doing": Research on Work-Based Learning in Advanced Technology Fields

Abstract

Although advanced manufacturing (AM) is growing throughout the country, rural communities are often excluded from this expansion because they lack skilled workers. To grow their AM workforce, rural employers and educators must document needed new professional competencies by understanding which skills can be taught on-the-job, as well as which knowledge, skills, and abilities are best gained through classroom learning and experiential learning. This enhanced understanding not only benefits employers’ hiring practices, but also can help career and technical education (CTE) program leaders improve curricula and expand learning opportunities to best meet future workers’ and employers’ needs.

In this study, we used multiple methods to triangulate industry competency model content to rural employers' perspectives on AM professionals’ desired competencies to two-year college AM program curricula. To extract competencies for entry-level AM jobs, we used text mining to extract, analyze, and compare the U.S. Department of Labor’s AM 2010 and 2020 Competency Models; we used these models because they reflect the complexity levels and topics that national AM industry professionals reported as technician needs. Then, we interviewed 10 rural AM employers in North Florida to capture their perceptions of the most important competencies for new technicians and used natural language processing (NLP) to analyze interview transcripts by identifying and extracting competency levels and topic areas. Finally, we used text-mining to extract course outcomes from AM program syllabi. Also using NLP, we compared these three data sets for alignments and gaps.

Our results suggest that rural employers prioritized knowledge, skills, and abilities that significantly differed from the competency models’ emphases and program content. We conclude that this incongruity has AM program design implications for educational policymakers and AM program administrators, as well as significant expectation-setting and new professional retention considerations for rural AM employers.

Authors

• Marcia Mardis, Florida State University

• Faye R. Jones, Florida State University