In higher education, significant efforts have been made to improve student success outcomes. In this dissertation, two important problems related to student academic success are considered. The curriculum plays a crucial role in shaping student success. Curricular complexity has been shown to be inversely related to the graduation rate of students. In the first problem, we consider analytical methods that may be applied during backwards curriculum design in order to reduce curricular complexity. Another common scenario in higher education is that students are likely to transfer between institutions before they attain their degrees. However, it is challenging to design effective and efficient transfer pathways for transfer students. In the second problem, methods on addressing the challenge in designing transfer pathways are considered. In order to reduce curricular complexity, the rationale for the approach taken here follows from the fact that by rearranging the learning outcomes among the courses in a curriculum, the overall structure of a curriculum can be changed. It has been previously demonstrated how curricular changes, that involve judicious rearrangement of learning outcomes among the courses in an engineering curriculum, can reduce curricular complexity and thereby dramatically improve student success outcomes. Thus, the ability to reduce curricular complexity through backwards design is a significant consideration for academic programs, and it should inform the work of curriculum committees. The restructuring of a curriculum, even when informed by assessment data, is often guided by expert intuition and gut feelings about what changes are likely to yield improvements. The methodology described in this dissertation can be helpful in providing analytical justifications that may validate the expected efficacy of these changes. More importantly, this methodology may be used to search the space of possible curricula in order to find more effective solutions that may not be immediately evident to curriculum committees. This capability is particularly important for STEM curricula, as they are among the most complex in all of higher education, which significantly increases the difficulty of searching through the solution space in order to find potential curricular improvements. The methodology described in this dissertation augments the backwards design process, a current staple in curriculum design practice, by automating the search through feasible curricular solutions. We first formally define the Optimal Learning Outcomes Assignment (OLOA) problem, which involves assigning learning outcomes to courses during backwards design in ways that minimize the complexity of the resulting curricula. The OLOA problem is then shown to be strongly NP-complete, which, given the size of the search space, makes finding optimal solutions impractical over realistic curricula. An integer quadratic programming approximation algorithm for the OLOA problem is then described that yields novel solutions to important curricular design challenges within a reasonable running time. This algorithm effectively produces practical, efficient, and novel solutions for attaining the most important leaning outcomes as is demonstrated using a number of undergraduate curricula. It is common for students to transfer from one institution to another for various reasons, with the hopes that prior earned credits will be accepted at the intuitions they are transferring into. A typical scenario for transfer students involves those admitted to community colleges planning to later transfer to 4-year universities in order to pursue bachelor's degrees. Research on the transfer process indicates that, on average, transfer students lose credit hours equivalent to one year of coursework. Given the vast number of transfer students nationwide, such significant loss of credit hours represents a significant waste of valuable educational resources that should be avoided in order to improve student success outcomes. However, finding efficient and effective transfer pathways between institutions is challenging, particularly when accounting for program requirements that are constantly changing, students changing their major plans, the creation of new courses, etc. Crafting a suitable plan for transfer students demands expert knowledge, effort, and sometimes collaboration among multiple institutions. Managing all of this complexity manually is partly accountable for the credit loss issue mentioned above. To gain a deeper understanding of this challenge, we formally define the Optimal Transfer Pathway (OTP) problem, which involves finding a two-year to four-year degree plan that can be used to satisfy the degree requirements from both a community college and a four-year university using a minimum number of credit hours. We consider the significant data requirements necessary to solve the OTP problem. These include collecting the Boolean formula that describes all degree requirements, the courses that may be used to satisfy these requirements, as well as the transfer equivalencies that exist between institutions. The combinatorics associated with finding degree pathways between any associates degree and any bachelor's degree make this problem exceedingly difficult, and a proof of the NP-Completeness of the OTP problem is provided. Thus, solving this problem through an exhaustive search in a reasonable amount of time is computationally infeasible. To address this issue, we treat the OTP problem as an assignment problem that seeks a feasible course-to-degree requirements assignment. In particular, we describe a 0-1 integer quadratic programming algorithm for the OTP problem that returns near optimal transfer plans in a reasonable timeframe. Experiments with this algorithm, using real degree requirement data from two Arizona institutions, have yielded insightful results regarding degree completion plans. The solution was created using the JuMP mathematical optimization modeling language, implemented in the Julia programming language, and is solved using a commercial optimizer. The analytical results returned by this system allow students to clearly understand how each course is used to meet specific degree requirements, which courses are transferable or not, and the reasons for their transferability. Additionally, it facilitates the consideration of multiple completion plans by advisors, which is beneficial for future degree requirement designs. We conclude with a discussion on leveraging this algorithm to meet the more tailored requirements of individual transfer students. [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/en-US/products/dissertations/individuals.shtml.]