A Statistical Analysis of Students' Attitudes and Achievement in Introductory Statistics Courses
The Millennial Generation has been phasing out of undergraduate classrooms since 2013 and is 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, a two-fold data collection design has been implemented in undergraduate statistics classes at the University of Rhode Island. In the first round of data collection during the spring 2016 semester, survey and grade data was collected from an introductory biostatistics class pertaining to 146 students. Results from the analysis including the use of longitudinal generalized linear mixed models, hierarchical linear models and regression trees indicate a relationship between time and student performance throughout the semester, as well as a relationship between students’ starting attitudes and their performance and a potential group structure in the class based on their attitudes.^ This first round of data collection and analysis lead to interesting results about students starting attitudes and the effect on their performance. To further explore these results and extend them to more than one course, a second round of data collection was completed during the spring 2017 semester. Principal component analysis in connection with regression analysis indicate a relationship between students’ starting attitudes and their course performance. Cluster analysis indicates a two group structure in starting attitudes of the students in each course, with each cluster showing different achievement and learning preferences.^
Kaitlin M Dio,
"A Statistical Analysis of Students' Attitudes and Achievement in Introductory Statistics Courses"
Dissertations and Master's Theses (Campus Access).