This project will establish a partnership between the mathematical sciences units at the University of Texas at Dallas (UTD) and the University of Texas Rio Grande Valley (UTRGV). UTD, situated in a region that is undergoing vigorous economic growth, is a diverse university with vibrant PhD programs in Mathematics and Statistics. UTRGV is a Minority Serving Institution (MSI), with over 87% Hispanic student body, located in a region experiencing population growth. UTRGV does not currently have a PhD program in Mathematical Sciences. The challenges faced by UTRGV undergraduate students vis-a-vis pursuing a doctoral program are typical of an MSI, including students' lack of preparation in advanced courses that are prerequisites for success in PhD programs and lack of awareness about careers in the Mathematical Sciences and graduate school admission process. A lack of professional internships in the Rio Grande Valley is a specific challenge for students from that area. This project addresses these challenges and develops a sustainable and scalable pipeline of well-prepared applicants, most of whom are expected to be from underrepresented groups, for PhD programs in the Mathematical Sciences.
The project amalgamates a number of evidence-based techniques known to be effective in increasing participation of underrepresented groups in doctoral programs into one coherent program. It leverages the use of technology and experience at both universities in active learning pedagogy to offer the most advanced undergraduate courses in a synchronized, blended setting. Its activities will be continuously evaluated via a rigorous and comprehensive assessment process. Its specific aims are: to develop hybrid versions of five of the most advanced undergraduate Mathematical Sciences courses; to conduct summer boot camps to cultivate research, problem-solving, and communication skills; to facilitate connections between UTRGV and UTD students and industries in the Dallas-Fort Worth area; and to help participating faculty and students develop an understanding of implicit bias and train them to overcome it.
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.
