2024 ASEE Annual Conference & Exposition

In higher education 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. In this paper we consider the role that data and analytics can play in addressing this problem.

To gain a deeper understanding of this challenge, we first 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 4-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 formulas that describe 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.