This Research Full Paper presents two examples of doctoral engineering attrition. To date, little research has been conducted on the many compounding factors that lead to attrition in graduate programs. In this paper, we present the narratives of two doctoral PhD students, Kelsey and Amy, who were deciding on departing from the engineering PhD. These narratives embody a deeper investigation of academic self-concept development through graduate school, with a focus on the decision-making processes to continue in the PhD program or decide to depart with a Master’s degree. At the time of the interviews, both participants were still enrolled in their programs, but one had definite plans to depart and left shortly after the interview. This study is one of the first that highlights the role of the Master's degree as an off-ramp from the engineering doctorate and lends insight to narratives surrounding attrition in engineering: Despite academic success in their courses and successful research progress, these participants decided to depart even after passing significant milestones such as qualifying exams. This research presents the beginning of a larger research project with a goal of generating a more complete narrative of the attrition process for the students, with an explicit focus on Master's-level departure.
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What I Wish I Had Known Early in Graduate School but Didn’t
Begin with the end in mind!1 PhD students in artificial intelligence can start to prepare for their career after their PhD degree immediately when joining graduate school, and probably in many more ways than they think. To help them with that, I asked current PhD students and recent PhD computer-science graduates from the University of Southern California and my own PhD students to recount the important lessons they learned (perhaps too late) and added the advice of Nobel Prize and Turing Award winners and many other researchers (including my own reflections), to create this article.
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- Award ID(s):
- 1837779
- PAR ID:
- 10193994
- Date Published:
- Journal Name:
- AI Magazine
- Volume:
- 41
- Issue:
- 1
- ISSN:
- 0738-4602
- Page Range / eLocation ID:
- 90 to 100
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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