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CAREER: Theoretical Understandings to Proactively Uplift Economically Disadvantaged Engineering Students
NSF
About This Grant
The project will focus on understanding and modeling career decisions of engineering undergraduate students and engineering faculty at different points in their careers and considering their income backgrounds. Using insights from scarcity theory, a concept from behavioral economics, the study will examine how limited resources affect career decision-making in engineering fields. It will look at how financial stress influences what students prioritize when making critical career choices in engineering. From the findings, educational tools will be developed to help train educators and advisors on ways to support economically disadvantaged students to succeed in engineering careers. The study will include innovative research and education to develop and build capacity for a resilient technical engineering workforce. The project will employ a longitudinal mixed methods design to investigate how engineering students from different income levels make critical engineering career decisions, to what extent these decisions are impacted by a scarcity mindset, and how engineering students’ decision-making changes over time. Multidisciplinary research using scarcity theory from behavioral economics and social cognitive career theory from vocational psychology will be integrated to understand engineering students’ career decision-making. The research project will be divided into three phases. The research questions that will be addressed include: (Phase 1) What factors contribute to undergraduate engineering students’ decisions to persist in an engineering degree? (Phase 2) Despite economic disadvantage, how do undergraduate engineering students make career decisions as they persist in an engineering degree, and how do these decisions change, if at all, as they progress in their studies? (Phase 3) What strategies do engineering faculty use to consider economic disadvantages of undergraduate engineering students in their courses? In Phase I, a survey instrument will be developed and tested by adapting questions from Social Cognitive Career Theory and Scarcity Theory. The survey will be distributed widely to engineering colleges. Data from the survey aims to identify the ways scarcity theory factors influence critical career decisions among engineering students and faculty. Phase 2 will employ qualitative methods to track the evolution of critical career decisions of low-income engineering students over time. Data from Phase 1 and Phase 2 will be corroborated to contribute to existing career development models and gain a deeper insight into career decision-making strategies of economically disadvantaged engineering students. Phase 3 will investigate how educators consider low-income students in course design and delivery. The research findings from all three phases will be translated into educational interventions for engineering faculty, advisors, and teaching assistants. Professional development workshops will be designed and implemented for graduate student TAs in engineering education courses and for faculty in professional development workshops. To facilitate active learning in the workshops, interactive decision trees and illustrated novelas will be designed using the research findings. Expected outcomes include educational lesson plans for interactive early career faculty workshops and TA trainings, interactive web-based decision trees for engineering counselors and advisors, and a brief with recommendations for adjustments to institutional support resources and course structures for engineering college administrators and leaders. The educational tools will be available for public use and research findings will be shared widely through journal publications and conference presentations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Focus Areas
Eligibility
How to Apply
Up to $655K
2030-08-31
One-time $749 fee · Includes AI drafting + templates + PDF export
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