Increasing Feedback from Generation Z: Students' Attitudes and Achievement in an Introductory Biostatistics Course (Natallia Katenka - URI)

The Millennial Generation is phasing out of undergraduate classes and being replaced by the technologically savvy and visual learners of Generation Z. To help to increase our understanding of the learning needs and attitudes of this new population of students, we collected survey and grade data in an introductory biostatistics course over two semesters (Spring 2016, Spring 2017) at the University of Rhode Island. For Spring 2016 data, our purpose was three-fold. First, to increase the amount of immediate feedback collected from students by implementing weekly quizzes. These quizzes were analyzed using longitudinal mean response profiles and generalized linear mixed models to discover a significant effect of time on the student performance, but not of grade incentives. Next, students attitudes towards statistics were analyzed to determine how the starting attitudes effected performance using hierarchical linear models to find a significant effect of starting affect and cognitive competence on students final grades. Finally, regression trees were utilized to identify groups of learners who increased their attitude throughout the semester dependent on their starting attitude and final grade. In addition to the attitude component and students’ final grades, the follow-up study included the collection of information pertaining to students’ learning and teaching preferences, as well as their collaboration throughout the semester. Preliminary analysis of new data was performed using principal component analysis, clustering, structural equation modeling, and network data modeling revealed suggestive grouping patterns among students who share similar teaching/learning preferences and attitudes toward the subject.