BackgroundGraduate engineering student attrition is prevalent, but most literature that studies graduate attrition is accomplished in disciplines outside of STEM or engineering, yielding an incomplete understanding of either attrition or persistence.Purpose/HypothesisThe purpose of this article is to investigate the relationships between motivators of attrition for engineering graduate students.Design/MethodData were collected using an online Web‐scraping “bot” that mines data from the online forum Reddit. The anonymous textual forum threads collected were qualitatively analyzed through open‐coding methods.ResultsThe emergent themes reveal the interconnectedness between the roles of the advisor, student perception of cost, their support network, goals, their perceptions of how others perceive them, and quality of life and work. Our model is flexible in that it illuminates underlying combinations of factors that can influence student attrition.ConclusionThis study provides a framework by which various stakeholders can approach the support and education of graduate students, including mentoring students both toward or away from graduate school per the student's goals.

This research paper presents methods by which researchers can harvest data from social media forums as a way to gain insight on sensitive issues or populations. In the present research, we are interested in studying doctoral attrition, which is a complex and multifaceted phenomenon that poses practical significance to funding agencies, advisors, and students themselves. Sampling non-completers is difficult, and researchers generally find it difficult to collect nationwide narratives of attrition. This paper presents a novel method for studying attrition using the publiclyavailable online forum Reddit.com to collect first-hand accounts and authentic narratives of attrition. These often-anonymous online discussions offer a unique view into the authentic thoughts of engineering graduate students considering leaving their program, throughout the decision-making process. This paper proposes a method to efficiently collect and parse opensource information into coherent narratives across "posts" or "threads" of conversation using data mining tools. The underlying methodology developed is based on achieving a holistic view of the discourse patterns and authentic narratives surrounding attrition, which in turn allows researchers to capture meaningful, authentic, and credible emergent themes unbiased by social response. We present a short summary of results to show the dominant narratives of attrition achieved through this method; however, the main focus of this paper is to present the method itself, which has the potential to be extended and modified to aid in other large data mining efforts to answer other research questions related to sensitive topics.

Dakota, her M.S. in Aeronautical and Astronautical Engineering and Ph.D. in Engineering Education from Purdue University. Her research interests include graduate-level engineering education, including inter-and multidisciplinary graduate education, online engineering cognition and learning, and engineering communication.

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