This quantitative research study considered three academic factors, seven student-level variables, and four success measures across ten years of academic data related to undergraduate remedial mathematics beginning with the 2009-2010 academic year and ending with the 2018-2019 academic year. Secondary data was used to determine if there was a relationship between the academic factors and/or the student-level variables and the success measures. The traditional enhanced with technology model, the emporium model, and the co-requisite model were the academic factors considered. The student-level variables included gender, ethnicity, Pell Grant Status, student type or age, first-generation status, SAT Math score, and major. The success measures investigated were pass, retention, persistence, and graduation rates. Data was analyzed for all ten years (2009-2019), as well as categorized by academic years for each of the three teaching models (2009-2010 to 2013-2014, 2014-2015 to 2015-2016, 2016-2017 to 2018-2019). Bivariate correlations demonstrated six of the seven student-level variables were correlated to pass rates in remedial mathematics courses. Of the three teaching models, the emporium model and the co-requisite model were positively correlated with pass rates in remedial mathematics while a negative correlation was reported for the traditional enhanced with technology model. Binary logistic regression results varied for each of three time frames related to the academic years the three teaching models were in practice. Of the student-level variables, only age was not a statistically significant predictor of retention. The traditional enhanced with technology model, the emporium model, and the co-requisite model indicated higher odds for retention. Under the success measure of persistence, age and major were not statistically significant. The co-requisite model displayed higher odds for persistence. Considering graduation rates as the final success measure, age was the only student-level variable not found as a significant predictor. Both the traditional enhanced with technology and the emporium model reported higher odds for graduating. Findings from this study have implications for understanding the population of students requiring undergraduate remediation in mathematics along with adding to the literature on redesign efforts in remedial mathematics. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com.bibliotheek.ehb.be/en-US/products/dissertations/individuals.shtml.]