Using Observational Learning Theory to Interpret How Engineering and Computer Science Faculty Learn to Mentor Postdoctoral Scholars
Conference
Bahnson, M, Berdanier, CGP, Ross, MS. (2022). Using Observational Learning Theory to Interpret How Engineering and Computer Science Faculty Learn to Mentor Postdoctoral Scholars
.
Bahnson, M, Berdanier, CGP, Ross, MS. (2022). Using Observational Learning Theory to Interpret How Engineering and Computer Science Faculty Learn to Mentor Postdoctoral Scholars
.
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. This work provides a unique window into postdoctoral training and mentorship, highlighting the need for more explicit expectations and plans for postdoc advisors.
