By Ivan Ramler (St. Lawrence University)
Information
The National Science Foundation’s S-STEM program supports the success of low-income, academically talented students in STEM fields. While large universities can recruit and support students within a single discipline, smaller liberal arts institutions often face challenges in attracting and retaining students, particularly in fields like statistics and data science, where majors may not be firmly established.
At St. Lawrence University, an interdisciplinary team created the Liberal Arts Science (LAS) Scholar Program, funded through two NSF S-STEM grants (Award Numbers 1458712 and 1930380). This program supported multiple cohorts of low-income, high-achieving undergraduate students in STEM through financial assistance, coursework, mentoring, and research experiences. A key component involved integrating statistics throughout the program.
This poster presents lessons learned from implementing the LAS Scholar Program, including strategies for incorporating statistical thinking into interdisciplinary STEM experiences and fostering student engagement in mentored research, with an emphasis on statistics and data science students in the program. We will also share the results from surveys on the impact of mentored research within the program. Our goal is to help faculty at similar institutions use our approach as a model to build or enhance S-STEM programs that integrate statistics and data science while addressing the unique challenges of liberal arts settings.