In the project, we investigate the drivers of peer collaborations in two undergraduate statistics courses at the University of Rhode Island.
Academic-based peer relationships can offer students diverse perspectives, knowledge sharing, social support, accountability and help students develop valuable interpersonal and social competencies, advantageous to future professional success. The main focus of this project is on investigation of the drivers of peer collaborations in two undergraduate statistics courses at the University of Rhode Island. The primary tools include network-based visualization, characterization and modeling methods including Exponential Random Graph Models (ERGMs). We analyze the data collected in the Spring 2017 semester from multiple sections of two major statistical courses: STA 307: Introductory Biostatistics (STA 307) and STA 308: Introductory Statistics (STA 308). Introductory and pre-course attitude surveys were administered at the start of the semester and exit and post-course attitude surveys at the end. Collected data contains student demographics, study habits, learning preferences, pre-and post-course attitudes, stress levels, and student collaborators’ names. Students enrolled in STA 307 share many courses together and exhibit considerably more collaborations than students in STA 308 that represent a more diverse range of majors. The modeling results in both courses suggest students are more likely to collaborate with classmates in their recitation section and with students who share the same type of housing; in STA 307 students also tend to collaborate if they share similar characteristics (homophily), namely athletes, non-athletes, in-state students, out-of-state students, and male. The significance of the geometrically weighted edgewise shared partnerships statistic in both course models suggests the presence of transitivity, meaning that there is a significant proportion of students studying in small groups of three. Findings from this study can help instructors to tailor course structure and teaching methodologies to this new generation of students.
The main results will be presented in the Joint Statistical Meetings 2021.