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Using Observational Learning Theory to Interpret How Engineering and Computer Science Faculty Learn to Mentor Postdoctoral Scholars

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2022 ASEE Annual Conference & Exposition


Minneapolis, MN

Publication Date

August 23, 2022

Start Date

June 26, 2022

End Date

June 29, 2022

Conference Session

ERM: Mentoring for Everyone! And Let's talk about Graduate Students

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Paper Authors


Matthew Bahnson North Carolina State University at Raleigh

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Postdoc in Engineering Education at Penn State with Catherine Berdanier.

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Catherine Berdanier Pennsylvania State University

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Catherine G.P. Berdanier is an Assistant Professor of Mechanical Engineering at Pennsylvania State University and is the Director of the online Master of Science in Mechanical Engineering Program at Penn State. Her research interests include graduate-and postdoctoral-level engineering education; attrition and persistence mechanisms, metrics, policy, and amelioration; engineering writing and communication; and methodological development for nontraditional data. Her NSF CAREER award studies master’s-level departure from the engineering doctorate as a mechanism of attrition. Catherine earned her B.S. in Chemistry from The University of South Dakota, her M.S. in Aeronautical and Astronautical Engineering from Purdue University, and Ph.D. in Engineering Education from Purdue University.

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Monique Ross Florida International University

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Assistant Professor, Knight Foundation School of Computing and Information Sciences and STEM Transformation Institute at Florida International University, research interests include broadening participation in computing through the exploration of: 1) race, gender, and identity in the academy and industry; 2) discipline-based education research that informs pedagogical practices that garner interest and retain women and minorities in computer-related fields. She uses her scholarship to challenge the perceptions of who belong in computing.

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In this research paper, we describe faculty development as mentors to postdoctoral fellows (postdocs). Postdoctoral fellowships have become a significant step in academic and industry positions, while little research investigates the purpose of a postdoc position, the experiences of postdocs, and how faculty develop as postdoc advisors. As part of a larger project investigating postdoc mentorship, nineteen semi-structured qualitative interviews with faculty advisors uncovered themes about how postdoc advisors learn to mentor and advise postdocs. Faculty from U.S. and Canadian universities completed 60-minute online interviews. Participants had varying experience and expertise in advising postdocs. Observational learning theory provides a framework for identifying the process of learning from observation and the replication of mentors’ past experiences. Open and axial coding was used to identify faculty experiences that informed how they mentored their postdoctoral fellows. Faculty who had completed a postdoc as part of their training reflected on their experiences, often identifying positive and negative experiences they used to guide, mentor, and plan the development of the postdocs they advise. Faculty who did not complete a postdoc used doctoral and industry experiences to inform their decisions. The results of this work provide a unique window into postdoctoral training and mentorship, highlighting the need for more explicit expectations and plans for postdoc advisors.

Bahnson, M., & Berdanier, C., & Ross, M. (2022, August), Using Observational Learning Theory to Interpret How Engineering and Computer Science Faculty Learn to Mentor Postdoctoral Scholars Paper presented at 2022 ASEE Annual Conference & Exposition, Minneapolis, MN.

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