NSF Award
2321390
While there has been important research on improving retention for Black, Latinx, and economically disadvantaged students in engineering, these studies typically do not account for factors associated with being a post-traditional student. Traditional students are 18-24, First Time in College, enrolled full-time, and reside on campus. On the other hand, post-traditional students can be identified with one or more of the following factors: being 25 years of age or older, enrolled part-time, working to support oneself or family, and/or a commuter student. Consideration of these factors is important both because they add significant complexities for a population that is already underserved in engineering (i.e., Black, Latinx, and economically disadvantaged learners), and because post-traditional undergraduates now comprise the majority of students in colleges and universities. This population shift has given rise to a new term in higher education literature for this group: “Post-traditional” students. “Post-traditional” instead of “non-traditional” acknowledges that this population now represents a new normal and that these undergraduates are no longer an aberration. Aligned with the NSF Broadening Participation in Engineering goals and target demographics, along with these population changes in higher education, a shift is needed to understand how post-traditional factors impact Black, Latinx, and economically disadvantaged students. This project will collect and analyze data on underserved post-traditional engineering undergraduates from three racially and ethnically contrasting institutions. The project will have wide implications; post-traditional students are in all institutions and need creative support, and their needs are known if no one asks. This research will take a transformative approach to not only understand those needs but also position faculty and staff to partner with students to find solutions. This should lead to novel programs and policies that the engineering community can implement as well as evaluate in future studies. Resources and findings will be disseminated with institutional partners such as American Society for Engineering Education and American Council on Education to ensure findings are both widely understood and implemented.<br/><br/>The goals of this transformative cyclical mixed methods research study will be two-fold. First, seek to understand factors that impact the retention and persistence of underserved post-traditional undergraduates in engineering. Second, aim to identify strategies that engineering programs can implement to be more inclusive of and responsive to these students’ needs. Motivated by these goals, the following research questions will be answered: 1) What factors impact underserved post-traditional students’ retention in engineering? (Quantitative); and 2) What are underserved post-traditional students’ experiences and perspectives regarding their persistence in pursuit of four-year engineering degrees? (Qualitative). The study is informed by the Model of Co-Curricular Support (MCCS) in engineering education, the students-as-partners conceptual model, and intersectionality. This three-year study will be conducted across three racially and ethnically different institutions: 1) A large public Hispanic-Serving Institution; 2) A small private Historically Black College/University; 3) A large public predominately White institution. A quantitative approach will assemble institutional data sets and collect survey responses from both students and faculty/staff. The multi-phase qualitative research design will consist of focus groups with students to understand their perspectives. It will also involve stakeholder action meetings in which students and faculty/staff work together to collaboratively generate recommendations for policy and practice. A key outcome from this study will be a large dataset on post-traditional learners from these institutions, which will include several more post-traditional variables than are available through the MIDFIELD engineering data file. The dataset will also allow the team and future projects to illuminate differences across student populations depending on the number of post-traditional variables that apply to them.<br/><br/>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.
