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Abstract BackgroundAlthough most engineering graduate students are funded and usually complete their degrees faster than other disciplines, attrition remains a problem in engineering. Existing research has explored the psychological and sociological factors contributing to attrition but not the structural factors impacting attrition. Purpose/HypothesisUsing systems theory, this study seeks to understand nuance in how underlying structural causes affect engineering graduate students' attrition experiences in ways that may differ from their official reasons for departure. Design/MethodsData were collected through semi‐structured interviews with seven departing or already departed engineering doctoral students from R1 graduate programs across the United States. Using thematic analysis, root cause analyses were conducted to understand participants' attrition experiences to explore how structures influence causes of departure. ResultsThe ways participants discuss root causes of their departure indicate differences in formal reasons for departure and underlying causes of departure. We highlight the role of informal and formal policy as root causes of a different attrition rationale often passed off as interpersonal issues. When interpreted as evidence of structural issues, the causes of departure show ways in which action–inaction, policy–“null” policy serve as structural features governing student attrition decision processes. We also highlight a form of benign neglect toward struggling graduate students. ConclusionThis study reveals important nuances underlying face‐value reasons of attrition indicating foundational structural issues contributing to engineering graduate student attrition. Coaching faculty in team management and encouraging close revision of departmental policies could help mitigate students' negative graduate experiences and decrease unnecessary attrition. 

This paper explores how mechanical and aerospace engineering (MAE) students understand and improve their data proficiency throughout their engineering curriculum. Data is essential for engineering students to be proficient in handling, as it is involved in every aspect of engineering. With the growing ubiquity of data and data analysis in all engineering fields, engineering students need to learn and master data skills to be competitive in the current and future job market. However, there is a lack of research on how non-computer science or software engineering majors perceive data proficiency and how they seek opportunities to develop data skills, especially as it relates to specific subdomains. In this paper, we investigate how students perceive data proficiency and how they develop using interview data from N = 27 MAE students at a research institution in the southeastern United States. Using the How People Learn framework, we analyzed the data through thematic analysis methods with a postpositivist approach, considering the bounded context of this study. The results show that MAE students value data proficiency as a crucial skill for their future careers and recognize its importance in making evidence-based engineering decisions. The study also reveals that, even though data proficiency is often a “hidden competency,” MAE students intuitively find various ways to enhance their data skills. These findings may help engineering educators to tailor their instruction to their students’ needs, address misconceptions about data and data proficiency, and prepare a data-literate future engineering workforce.