Improving Transfer Student Outcomes Through Linked Learning Communities in Math and Science

This project will contribute to the national need for skilled scientists, mathematicians, engineers, and technicians. It will do so by supporting the retention and graduation of high-achieving, low-income students with verified financial need at a large California community college, Diablo Valley College. The transfer rate of Diablo Valley College students into four-year universities is 67% higher than the national average.

Even so, low-income STEM students at the College struggle to balance their financial challenges with their educational commitments. This project will address this need by giving scholarships to 30 different students who are pursuing Associate degrees in Civil, Electrical & Computer, or Mechanical Engineering, Natural Science, Mathematics, Biology, and Physics.

The Scholars will enter in cohorts of 10 students and receive up to three years of scholarship support, beginning with their entry into a STEM pathway at the College and extending to their transfer to a four-year program. In addition to scholarships, the project will support Scholars through a linked learning community. This learning community will include their math and science instructors, a student retention specialist, and faculty mentors.

It is expected that this support will foster community among the Scholars and the College staff, while providing professional resources to meet Scholars’ individual needs. The project team predicts that this model of support will lead to increased student motivation and preparation for successful transfer. Community colleges are a frequent entry point into higher education for low-income and underrepresented students.

Thus, by addressing common points of attrition that occur early in STEM curriculum pathways, this project can contribute to a more diverse and representative STEM workforce.

The overall goal of this project is to increase STEM degree completion of low-income, high-achieving undergraduates with demonstrated financial need. The project will study how a comprehensive cohort model and course-pairing method of instruction may address a student’s integration experiences across academic, social, professional, and institutional domains.

The project will investigate how these different integration experiences impact community college students' foundational STEM course success, persistence, graduation, and transfer, with particular attention to students from groups traditionally underrepresented in STEM. The hypothesis is that the cohesion of the student cohort with each other and with mentoring faculty partners will provide more meaningful early integration experiences that, in turn, increase student success in early foundational STEM coursework.

This increased success will then translate to enhanced persistence and transfer rates. The project will explore the impacts of the financial and social benefits of the support model through results from student journaling, focus groups, and student outcome data. Through its mixed-methods approach, the project plans to generate rich qualitative data that can advance understanding of factors that increase student retention and persistence at two-year colleges.

The project leadership team will disseminate critical findings, methods, and tools locally through professional development activities as well as at state and national conferences. Project evaluation activities will assess the effectiveness of the linked learning model in improving student outcomes, persistence, and transfer rates, the College’s effectiveness in implementing the learning community model, and the extent to which knowledge generation and dissemination efforts were used to transfer knowledge.

This project is funded by NSF’s Scholarships in Science, Technology, Engineering, and Mathematics program, which seeks to increase the number of low-income academically talented students with demonstrated financial need who earn degrees in STEM fields. It also aims to improve the education of future STEM workers, and to generate knowledge about academic success, retention, transfer, graduation, and academic/career pathways of low-income students.

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.