Targeted Infusion Project: Infusing Data-Centered Pedagogy and Data-Analytical Skills into Introductory Statistics

The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP), through Targeted Infusion Projects, supports the development, implementation, and study of evidence-based innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue STEM graduate programs and/or careers. Accordingly, in line with recommendations of the National Academies, North Carolina Agricultural & Technical State University (NCA&T) seeks to modernize its introductory statistics course through a redesign that involves the infusion of data science and big data analytics components.

This project seeks to achieve three main goals: enhance students’ statistical knowledge and data-analytical skills gained from the Introductory Statistics course; create a pipeline for the new certificate and degree programs in Data Science by preparing and enticing STEM and non-STEM students taking introductory statistics to pursue data science education; and build a faculty cadre capable of, and committed to, teaching introductory statistics using data-centered pedagogy. Each year, the targeted Introductory Statistics course will enroll about 600 students.

The redesign of the course will consist of adding a virtual statistical computing lab to the course and adopting evidence-based data-centered pedagogy in teaching the lab and class sessions to enhance students’ understanding of fundamental statistical concepts and to train students to think structurally with data, make data-informed decisions, and expose students, early and frequently, to the elements of the data science workflow. The project will engage the introductory statistics faculty in a suite of professional development activities on adopting the data-centered pedagogy in their teaching.

The proposed redesign of introductory statistics is expected to improve students’ knowledge gained from the course, reduce the course repeat rates, help students develop the computational competencies needed for data science and STEM, increase the enrollment in the Data Science programs at the institution, and develop faculty teaching expertise.

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.