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Abstract BackgroundGiven high attrition rates and lack of interest in faculty careers, it is crucial to understand how doctoral engineering students conceptualize academia and academic careers. Purpose/HypothesisThis study aims to characterize the development of academic disenchantment among engineering students who have considered departure from their doctoral programs. Schema theory was used to explore how students develop and evolve in their conceptualizations of academia through their lived experiences. Design/MethodData were collected from 42 graduate students from research‐intensive universities across the United States who participated in qualitative, semi‐structured interviews investigating expectations for graduate school, experiences, attrition and persistence considerations, and career trajectories. The transcripts were thematically analyzed through open and axial coding to understand how students constructed their schemas of the academy. FindingsExperiences and quotations of four participants are presented to describe the results of the transcripts. Participants' misaligned expectations of their graduate program's values and practices, coupled with a lack of agency and support, led them to see their graduate programs as antagonistic to their short‐ and long‐term career success. Even for students who may likely persist through to PhD degree completion, the development of disenchantment dissuades students—even those who once desired a faculty career—from interest in the academy. ConclusionsBy understanding how disenchantment arose in our participants' experiences, we better understand how to equip students with resources that will help them navigate graduate programs. This research advances the literature by identifying underutilized opportunities to prepare students to cope with the challenges of engineering doctoral education. 

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.