This project traces the ways universities articulate a desire for diversity through the gateway genres of college admissions, composition course placement, and first-year-composition (FYC). Together, these genres serve as points of access for a theoretical study that seeks to better understand the ideological function of writing programs to socialize borderline college applicants into the rhetorically constructed role of a Diverse College Student. I focus on what I call bootstraps genres--reoccurring rhetorical situations that call for students to recount social hardships like racism and classism as personal hardships to be overcome through personal heroics. Despite being immersed in rhetorics of individualism, the college application essay, the directed self-placement guide, and the literacy narrative all call for the mimetic construction of disadvantage as an appeal to college-readiness. As new college students move through the initiation rituals of admissions, orientation, and FYC, they are presented with rhetorical tasks that are both raced and classed. Bootstraps genres ask students to first read the university's desire for diversity and then fulfill that desire through personal stories of difference and disadvantage.

This study analyzed the effects of a randomization-based inference teaching methodology on students’ content mastery in an introductory statistics college course. The sample was 125 undergraduate students from Miami Dade College, a large Hispanic Serving Institution in the Southeast. A pretest-posttest nonequivalent group design was used for the study. Students in the randomization-based teaching modality received exposure to simulation activities, specifically bootstrap confidence intervals and randomization test, that aim to enhance conceptual understanding of inferential statistics, an important component of statistical literacy. The instructional strategy was designed to trigger critical reflection that confronted students with their thinking and lead them through a process of reorganization, restructure, and improvement of their concepts. The 40 item Comprehensive Assessment of Outcomes in a first Statistics course (CAOS) instrument was used to measure students’ conceptual understanding of important statistical ideas along with a demographic and academic survey that collected data on student characteristics. A stepwise linear regression method was used to look at the effects of group membership while controlling for Pre-CAOS scores, age, gender, first generation, prior experience with statistics, student status (part/full time), native speaker, STEM or not-STEM major, Hispanic, highest math course taken in high school, and GPA. The full model showed that only Group and Pre-CAOS score were the only significant predictors of Post CAOS scores. None of the other variables were significant. The model was a significant predictor of Post CAOS score, F(2, 121) = 16.96, p The results supported the claim that the randomization-based teaching modality for inferential statistics help Hispanic students to achieve a better understanding of the learning outcomes associated with an undergraduate introductory statistics course.

The Bag of Little Bootstraps (BLB) was introduced to make the bootstrap method more computationally efficient when used on massive data samples. Since its introduction, a broad spectrum of research on the application of the BLB has been made. However, while the BLB has shown promising results that can be used for logistic regression, these results have been for well-balanced data. There is, therefore, an obvious need for further research into how the BLB performs when the dependent variable is unbalanced and whether possible performance issues can be remedied through methods such as Firths's Penalized Maximum Likelihood Estimation (PMLE). This thesis shows that the dependent variable's imbalances severely affect the BLB's performance when applied in logistic regression. Further, this thesis also shows that PMLE produces mixed and unreliable results when used to remedy the drops in performance.