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Facilitating Advanced Manufacturing Technicians' Readiness in the Rural Economy: A Competency-based Deductive Approach

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Conference

2021 ASEE Virtual Annual Conference Content Access

Location

Virtual Conference

Publication Date

July 26, 2021

Start Date

July 26, 2021

End Date

July 19, 2022

Conference Session

College Industry Partnerships Division Technical Session 2

Tagged Division

College Industry Partnerships

Page Count

15

Permanent URL

https://peer.asee.org/37171

Download Count

51

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Paper Authors

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Faye R. Jones Florida State University Orcid 16x16 orcid.org/0000-0001-6178-8143

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Faye R. Jones is a Senior Research Associate at Florida State University’s College of Communication and Information. Her research interests include STEM student outcomes and the exploration of student pathways through institutional research.

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Marcia A. Mardis Florida A&M University - Florida State University Orcid 16x16 orcid.org/0000-0002-2209-1498

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Marcia A. Mardis is a Professor and Associate Dean at Florida State University's College of Communication & Information and Associate Director of the Information Institute. Author of numerous publication and recipient of over two decades of federally funded research grants, Dr. Mardis' work focuses on professional identity creation, educational text and data mining, and technician education improvement.

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Priyanka Prajapati LPL Financial

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Priyanka Prajapati is a graduate student in information technology at Florida State University’s School of Information. Her research interests include Artificial Intelligence (AI), Natural language Programming (NLP) and Data Analytics .

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Pallavi Ramakanth Kowligi Florida State University

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Pallavi Kowligi is a Graduate Research Analyst at Florida State University’s College of Information. Her research interests include application of Natural Language Processing and Machine Learning techniques in the field of education.

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Abstract

While rural manufacturing job availability is growing throughout the country, rural communities often lack skilled workers. Thus, it is imperative for employers to validate needed new professional competencies by understanding which skills can be taught on-the-job as well as the knowledge and abilities best gained through classroom learning and experiential learning opportunities. This enhanced understanding not only benefits employers’ hiring practices, but also it can help Career and Technical Education (CTE) programs improve curricula and expand learning opportunities to best meet students’ and employers’ needs. In this study, we triangulated industry competency model content with rural employer perspectives on new advanced manufacturing (AM) professionals’ desired competencies (i.e., the level of skill sophistication in a particular AM work area). To extract competencies for entry-level AM rural jobs, we used a deductive approach with multiple methods. First, we used Natural Language Processing (NLP) to extract, analyze, and compare the U.S. Department of Labor’s AM 2010 and 2020 Competency Models because they reflect the levels and topics AM industry professionals nationally 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 middle-skill technicians. Interview transcripts were also processed using NLP to extract competency levels and topics; we compared this output to the AM Competency Model analysis results. We deduced that the most critical competencies identified by rural AM employers required direct classroom instruction, but there was a subset of skills obtainable through on-the-job training or other experiential learning. This study, with the goal of addressing employee shortages and increasing the number of technicians ready for the workforce, has implications for rural community colleges’ AM programs curricula and the role of experiential learning.

Jones, F. R., & Mardis, M. A., & Prajapati, P., & Kowligi, P. R. (2021, July), Facilitating Advanced Manufacturing Technicians' Readiness in the Rural Economy: A Competency-based Deductive Approach Paper presented at 2021 ASEE Virtual Annual Conference Content Access, Virtual Conference. https://peer.asee.org/37171

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