Advancing Student Success and Career Horizon through Learning Analytics Research (Advancing SCHOLAR)
Effective Years: 2023-2026
The goal of the Mid-Career Advancement program is to substantively enhance and advance the research program of mid-career scientists and engineers through synergistic and mutually beneficial partnerships, typically at an institution other than their home institution. The resources provided by the collaborator of this project will provide the principal investigator (PI) with both the opportunity to work with a team of individuals actively engaged in learning analytics research as well as access to a large complex data set suitable for such research. The PI, in turn, will provide additional research capacity for the existing learning analytics research team and bring an engineering perspective to the work that is not currently present. Moreover, the PI will conduct complementary work focused on the College of Engineering at the host institution and will later explore the application of vertical federated learning to address similar research questions in a different institutional context characterized by the lack of a consolidated data lake housing student data and the lack of resources available to create such a data lake.
While Black and Hispanic people currently comprise approximately 33% of the U.S. population, this portion of the U.S. citizenry comprised only 26% of all bachelor’s degrees, 22% of all master’s degrees, and 17% of all doctor’s degrees conferred in 2020. By 2060, the percentage of the U.S. population comprised of people who identify as Black or Hispanic is expected to grow to 43%. Despite this growth, if the dearth of Black and Hispanic Americans earning postsecondary degrees persists, the U.S. will not fully benefit from the additional innovation capacity represented by this untapped portion of its citizenry, nor will it keep pace with the number of professionals needed to maintain economic competitiveness worldwide. To redress this issue, the aim of this research program is to identify and understand the drivers underlying students’ time-to-degree completion and to understand the impacts of these drivers on the total cost of degree attainment – including any differential impacts based on students’ race, ethnicity, gender, socioeconomic status, or first-generation status. The discovered knowledge in this study will provide insights into the underlying drivers of inequities in 4- and 6-year graduation rates and learning analytics research approaches will be used to produce data-driven recommendations to reduce such inequities. The project is a step towards transforming the postsecondary system that students are entering by identifying and reducing the unique barriers encountered by specific student subpopulations in support of improved and equitable time-to-degree completion for all baccalaureate students, including Blacks and Hispanics.
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.