Mapping Engineering Leadership Research through an AI-enabled Systematic Literature Review

Meagan Kendall; Brian Novoselich; Meg Handley; Matthew Dabkowski

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Conference: 2022 ASEE Annual Conference & Exposition
Location: Minneapolis, MN
Publication Date: August 23, 2022
Start Date: June 26, 2022
End Date: June 29, 2022
Conference Session: Imagining the Research Agenda for ASEE LEAD
Page Count: 20
DOI: 10.18260/1-2--41855
Permanent URL: https://peer.asee.org/41855
Download Count: 760

Paper Authors

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Meagan Kendall University of Texas at El Paso

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Dr. Meagan R. Kendall is an Associate Professor in the Department of Engineering Education and Leadership at the University of Texas at El Paso. As an NSF Graduate Research Fellow, she received her M.S. and Ph.D. in Mechanical Engineering, with a concentration in Biomechanics, from The University of Texas at Austin. An engineering education researcher, her work focuses on enhancing engineering students' motivation, exploring engineering identity formation, engineering faculty development, developing integrated course sequences, and methods for involving students in curriculum development and teaching through Peer Designed Instruction. Dr. Kendall's scholarship emphasizes the professional formation of engineers, specifically through the development and application of the Contextual Engineering Leadership Development framework. Bringing together her work in engineering leadership development, curriculum design, and collaborative design, her current focus is on developing engineering instructional faculty as leaders of educational change at Hispanic-Serving Institutions. Dr. Kendall is the Division Chair of the Engineering Leadership Development (LEAD) Division of the American Society of Engineering Education and a Technical Program Chair for the Frontiers in Education Conference 2022.

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Meg Handley Pennsylvania State University

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Brian Novoselich United States Military Academy

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Brian J. Novoselich, Ph.D., P.E., is an active duty army officer, associate professor in the
Department of Civil and Mechanical Engineering, and the director of strategic plans
and assessment (G5) for the United States Military Academy (USMA) at West Point.
He earned his Ph.D. in engineering education at Virginia Tech in 2016. He holds Master’s
and Bachelor’s degrees in mechanical engineering from The University of Texas at
Austin and USMA respectively. His research interests include capstone design teaching
and assessment, undergraduate engineering student leadership development, and
automotive technologies. He is a licensed professional engineer in the Commonwealth
of Virginia.

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Matthew Dabkowski

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Colonel Matthew Dabkowski is currently an Associate Professor in the Department of Systems Engineering at the U.S. Military Academy at West Point. He has served in the United States Army for 25 years as an Infantry Officer, Operations Research Analyst, and Academy Professor. He is a graduate of West Point (B.S. in Operations Research) and the University of Arizona (M.S. in Systems Engineering and Ph.D. in Systems and Industrial Engineering). His research interests include applied statistics and simulation modeling.

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Abstract

Research in engineering leadership (EL) has seen substantial growth due to the increased recognition that engineering students’ leadership development is essential to their holistic development as engineers [1]. To support the continued growth of this nascent field, it is vital to examine its history and identify growth opportunities that accelerate EL development and broaden its impact. Identifying, codifying, and synthesizing the previous research in EL will provide crucial foundations for advancement and reduce the likelihood of redundant efforts [2]. A substantial portion of the research on EL is published through the American Society for Engineering Education (ASEE). In particular, EL thought leaders often publish through a division focused on supporting EL education, educators, and researchers, the Engineering Leadership Development Division (LEAD). This review explores how the focus of research in this field has evolved over the past 26 years within ASEE and identifies patterns in research populations, theoretical frameworks, and methods. Therefore, this research paper aligns with the Inform portion of the ASEE LEAD Division’s Inform/Develop/Explore/Assess strategic initiative framework and describes our systematic review of key EL literature. Using an Artificial Intelligence (AI)-enabled mixed-methods approach, modified from those outlined by Borrego et al. in [2], this systematic literature review is conducted on all papers published in the ASEE conferences’ proceedings between 1996 and 2021 with the word “leadership” in the title. We also include all papers published through the LEAD division. Papers included must focus on EL and be available in a finalized state from the ASEE PEER repository. The systematic review employs both quantitative and qualitative analysis to determine the state of knowledge in the field. This analysis uses AI to quantize word frequency in the abstracts and then a cluster analysis of the resulting matrices. We then compare these clusters to an adapted version of Terenzini and Reason’s college impacts framework of influences on student learning and persistence to identify potential areas for growth in the EL literature. We also map the clusters over time to explore the evolution in the research focus of the field since 1996, noting key events that may have contributed to shifts in focus. This systematic review of the EL literature is intended to advance knowledge of the field by categorizing prior work and detailing the evolution of research topics, methods, and populations. Thus, the results will expand future EL research by documenting the field's foundations, progression, and potential future.

Citation
Format

Kendall, M., & Handley, M., & Novoselich, B., & Dabkowski, M. (2022, August), Mapping Engineering Leadership Research through an AI-enabled Systematic Literature Review Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN. 10.18260/1-2--41855

TY - CPAPER
AB - Research in engineering leadership (EL) has seen substantial growth due to the increased recognition that engineering students’ leadership development is essential to their holistic development as engineers [1]. To support the continued growth of this nascent field, it is vital to examine its history and identify growth opportunities that accelerate EL development and broaden its impact. Identifying, codifying, and synthesizing the previous research in EL will provide crucial foundations for advancement and reduce the likelihood of redundant efforts [2].
A substantial portion of the research on EL is published through the American Society for Engineering Education (ASEE). In particular, EL thought leaders often publish through a division focused on supporting EL education, educators, and researchers, the Engineering Leadership Development Division (LEAD). This review explores how the focus of research in this field has evolved over the past 26 years within ASEE and identifies patterns in research populations, theoretical frameworks, and methods. Therefore, this research paper aligns with the Inform portion of the ASEE LEAD Division’s Inform/Develop/Explore/Assess strategic initiative framework and describes our systematic review of key EL literature. Using an Artificial Intelligence (AI)-enabled mixed-methods approach, modified from those outlined by Borrego et al. in [2], this systematic literature review is conducted on all papers published in the ASEE conferences’ proceedings between 1996 and 2021 with the word “leadership” in the title. We also include all papers published through the LEAD division. Papers included must focus on EL and be available in a finalized state from the ASEE PEER repository. The systematic review employs both quantitative and qualitative analysis to determine the state of knowledge in the field. This analysis uses AI to quantize word frequency in the abstracts and then a cluster analysis of the resulting matrices. We then compare these clusters to an adapted version of Terenzini and Reason’s college impacts framework of influences on student learning and persistence to identify potential areas for growth in the EL literature. We also map the clusters over time to explore the evolution in the research focus of the field since 1996, noting key events that may have contributed to shifts in focus. This systematic review of the EL literature is intended to advance knowledge of the field by categorizing prior work and detailing the evolution of research topics, methods, and populations. Thus, the results will expand future EL research by documenting the field's foundations, progression, and potential future.
AU - Meagan Kendall
AU - Meg Handley
AU - Brian Novoselich
AU - Matthew Dabkowski
CY - Minneapolis, MN
DA - 2022/08/23
PB - ASEE Conferences
TI - Mapping Engineering Leadership Research through an AI-enabled Systematic Literature Review
UR - https://peer.asee.org/41855
DO - 10.18260/1-2--41855
ER -