Faculty and Credit Transfer
For many students, transferring between community colleges and 4-year institutions is sometimes the only path to a bachelor’s degree. Community colleges provide a valuable service to students due to lower costs, closer proximity to home communities, and more flexibility for non-traditional students. In the fall of 2020, 4.7 million students attended 2-year community colleges, approximately 25 percent of all undergraduates. In addition, community colleges enroll higher percentages of first-generation students, students from low-income backgrounds, and students of color. Therefore, the ability to successfully transfer from a community college is particularly salient for addressing racial and socioeconomic inequities, such as gaps in transfer and completion rates, in postsecondary education.
Unfortunately, this path becomes less effective when credits completed at a community college are not applied to a degree program at a 4-year institution. Although many states have implemented policies to promote successful transfer between 2-year and 4-year public institutions, credit loss remains a significant issue for transfer students. Nationally, students lose 43% of their credits when transferring, which costs them time and money.
The Faculty Decisions & Credit Transfer Project
To address issues in transfer, the Faculty Decisions and Credit Transfer project was launched in 2022 through a partnership between MDRC, a nonprofit, nonpartisan research organization, the National Association of System Heads (NASH), and Computational Approaches to Human Learning (CAHL), a UC Berkeley research lab. This work is supported by Ascendium Education Group.
MDRC, NASH, and CAHL seek to examine the role of faculty and staff in the course articulation process, which involves evaluating whether a transfer course will be accepted for credit. We also seek to identify differences in how transfer credits are applied within different degree programs and, ultimately, to develop tools and solutions to mitigate barriers to credit transfer. The University of Texas (UT) System was selected to partner with MDRC to assess existing course articulation and applicability policies and procedures. A second project strand involves a partnership between NASH, CAHL, and the State University of New York (SUNY) that will use recent advances in artificial intelligence (AI) to address course equivalency problems. Both UT and SUNY have large universities where transfer students make up a significant percentage of undergraduates. Our goal is for students with limited time and resources to pursue more individualized, precise, and efficient paths to graduation and avoid the transfer “swirl” of retaking classes.
The Maze of Course Applicability and Degree Mapping Between Institutions
While the project is still in its early stage, we have some preliminary insights through our work with the University of Texas system. This phase focuses on transfer at three UT institutions chosen for the size of their transfer population, commitment to transfer students, and variations in size and populations served: UT Arlington, UT El Paso, and UT Tyler.
One critical transition point during the transfer student enrollment process requires the receiving institution to assess students’ course history and map it to academic program requirements. Early findings indicate that, while the equivalency of courses may be predetermined by articulation agreements or historical precedent, departmental faculty play a crucial role in course applicability decisions. They interpret and determine how the student’s prior learning maps to specific academic programs or degree requirements. In addition, credit application to degree requirements is a critical decision point for students, as the satisfaction of degree requirements ultimately allows them to progress toward timely degree completion.
Another insight that emerged from our early conversations with faculty and staff is the complexity of transfer processes. While faculty have purview over course applicability within departments and majors, a vast network of units across the institution also have a role in the credit transfer process. From the registrar to academic advising to provost offices, each administrative unit is just as integral to the transfer process as faculty decision-making about courses. Moreover, cross-unit communication can be challenging, exacerbating how students find the correct information at the right time.
Testing Artificial Intelligence (AI) Tools
The sheer magnitude of course credit equivalency comparisons that must be made between institutions to maintain a comprehensive and accurate database of articulations is prohibitive using existing approaches. Bridging credit between institutions at a much larger scale is necessary to ease the burden of navigating educational pathways for transfer students – particularly at under-resourced institutions. Prior research on algorithmically-generated course equivalency predictions at CAHL has found that combining course information with student enrollment patterns dramatically increases the accuracy of AI predictions. Still, while AI-assisted tools hold great promise, they must be implemented ethically and responsibly.
This project will engage with disciplinary faculty and transfer professionals to understand how algorithmically-suggested articulations can assist them in their work to support transfer students. We will design experiments to understand what type of course information best aids decision-makers in establishing equivalencies, how to implement AI-assisted advising tools into existing workflows, and explore theoretical questions around algorithmic aversion. The goal is to build a robust set of tools to expand pathways for students while easing burdens on faculty and staff caused by current data infrastructure limitations. We are engaging faculty, staff, and administrators at the UT and SUNY systems to co-design and optimize tools for sending and receiving colleges that improve the transparency and efficiency of course articulation and applicability.
Many people are involved in ensuring successful transfer student enrollment and degree planning at the new institution. However, it is unclear – especially for prospective transfer students – if and how these offices work together and how consistent processes and policies are across units. Future work aims to understand differences between academic departments – particularly differences in systems used to document and communicate student degree progress within and between sending and receiving institutions.
We will collaboratively develop tools, such as an AI-informed course equivalency platform, to support institutional decision-making. Ultimately, we believe this research will provide insights that allow systems and institutions to improve transfer, especially for students with low-incomes and student of color who have been disproportionately impacted by problems in the current system.