The purpose of this study was to examine the predictive power of Academic and Learning Analytics models on the persistence, retention, and graduation rates for students enrolled in higher education institutions in the United States. Specifically, this study is concerned with the relationships between the present usage of Academic and Learning Analytics models of Scholarship of Teaching and Learning, Course Management System, Social Networks Adapting Pedagogical Practice, and Graphical Interactive Student Monitoring (SoTL, SNAPP, and GISMO) and persistence, retention, and graduation rates in higher education institutions. Given the purpose of the research, a quantitative study was best suited for this study because the intention of the study was not to prove causality, but to examine possible relationships and differences between variables. Archival data from the web sites of sixty public institutions from three states in the Southern Region of the United States was collected. The variables of the study were the students' academic success as measured by persistence, retention, and graduation rates as anticipated by the use of Academic Analytics tool with one attribute of Scholarship of Teaching and Learning and Learning Analytics tools with two attributes of Social Networks Adapting Pedagogical Practice, Graphical Interactive Student Monitoring. Thus, the relationship between these variables will be predicted through the Multiple and Bivariate regression models for the purpose of establishing an intellectual result. The hypothesis generated by the research question on Academic Analytics and its attribute was tested using Bivariate Linear Regression analysis. The bivariate regression models resulted in a linear correlation coefficient of between R = 0.048 and R = 0.057, an indication that there is no significant relationship between the academic tools and student academic success. To test for hypotheses four, five, and six, a Standard Multiple Regression analysis was constructed. The findings of the analysis indicated that there is no statistically significant relationship between Learning Analytic tools and students' academic success. Thus, the recommendation is for a follow-up study to be conducted which will include a population of higher education institutions that is more global. Such a study, if conducted, would provide additional data to better understand the relationship between analytics model and the persistence, retention, and graduation rates of student enrolled in higher education institutions in the United States. [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.]