Date of Award
Master of Science in Statistics
Computer Science and Statistics
Generation Z, also known as iGen, (individuals born between the mid-1990s and early 2010s), characterized as tech-savvy, independent, and visual, is beginning to graduate college and enter the workforce. While significant research effort has focused on understanding the learning preferences of the preceding Millennial generation (individuals born between the early 1980s and mid-1990s), less is known about the way technology has influenced the educational expectations and learning preferences of Generation Z. A deeper and broader understanding of the way this generation learns would allow universities to modify and enhance course structures and teaching methodologies to suit this incoming generation of students better. In this thesis, we used secondary survey and performance data collected in all undergraduate statistics courses at the University of Rhode Island in Spring 2017 to distinguish the learning preferences of this new generation. Data collected contained student demographics, study habits, learning preferences, pre- and post-course attitudes, stress levels, and the names of student collaborators.
The goals of this study were to understand the main drivers of collaboration among Generation Z students taking introductory statistics courses and to identify differences in demographics, study habits, learning preferences, performance, and attitudes towards statistics between collaborators and independent learners. We used Network and Classical methods to characterize the network of students who collaborate and to distinguish collaborators from independent learners. Of the two courses explored as part of this study, the focus was on data collected in course, Introductory Biostatistics (STA 307), given the high response rate and collaborative structure of the network.
Descriptive statistics suggest that students enrolled in the same major are more likely to connect than students in disparate majors, perhaps because they have had opportunities to connect in other courses. Exponential Random Graph Models (ERGMs) were used to gain insight into and make inferences about the effects of endogenous and exogenous factors on the determinants of ties within a network. ERGMs fitted to the network of student collaborators indicate that students are more likely to collaborate with classmates in their recitation section and with students who share similar characteristics, namely other athletes, students living in the same type of housing, in-state students, and out-of-state students. Male students are also more likely to collaborate with other male students than females are to collaborate with one another. The significance of the geometrically weighted edge-wise shared partnerships (GWESP) statistic in the model suggests the presence of transitivity, meaning that there is a significant proportion of students studying in groups of three.
The results of the comparison between independent learners and student collaborators show that collaborators are more likely to complete practice exams. This is expected as students may be working through practice exams with their peers. Independent learners value the instructor's knowledge of the material to their learning, likely because they are more reliant on the instructor for understanding. At the same time, collaborators lean on their peers for knowledge sharing and support. Evidence does not suggest that student collaborators outperform independent learners in STA 307. While independent learners do not appear to be at risk of underperforming relative to collaborators, partnership with other students provides a natural support system, giving students an additional learning tool by which to learn.
Nowinowski, Isabel S., "STUDENT COLLABORATIONS IN INTRODUCTORY STATISTICS COURSES; A NETWORK STUDY" (2020). Open Access Master's Theses. Paper 1843.