Implications for policy and practice:

1. Institutions, funders, and state and federal agencies need to support rigorous research and the use of disaggregated data to improve transfer students’ outcomes. There is a lack of evidence about what truly matters for transfer student success. Additional, rigorous quantitative and qualitative research are needed to build the evidence base and improve learner mobility.^[1]
2. Improve institutional transfer policies and practices through constructive engagement with institutional data throughout learners’ journeys. Regular analysis and use of data on students’ outcomes and assessments by institutions can help educators understand who learners are, how to support successful transfer, and how to target that support.
3. Reduce bias by using data to evaluate students’ transfer credits. Requiring the use of data in credit evaluation has the potential to reduce subjectivity and bias that may currently exist in learning mobility practices.
4. Invest in quality technology and its implementation to improve the use of data in the evaluation of transfer credits. When implemented well, technology aimed at automating, standardizing, or accelerating processes such as credit and transcript evaluation can increase efficiency and help invested parties refine policies and practices related to learning mobility.

The ability for learners in postsecondary education, including career and technical pathways, to transfer and apply credit earned for past learning from a variety of venues—including postsecondary institutions, enrollment in college credits while in high school, work-based learning, and military experience—is a critical strategy for obtaining outcomes such as a bachelor’s degree or certificate, which can lead to positive future outcomes such as increased earnings.^[2] For decades, research has shown the benefits of postsecondary degrees and professional training on social, economic, personal, and societal outcomes.^[3]

Nearly 40 percent of students transfer from one institution to another at some point during their postsecondary journey. Unfortunately, many students lose at least some of their credits when they try to transfer: On average, 43 percent of credits students have already earned are not accepted by their new institutions. ^[4] Decisions about which credits transfer are often made based on information and criteria that are not always public. As a result, students continue to face significant barriers to getting credit for previous learning of all kinds accepted, which can result in increased cost and time to earn a degree. Although many states have implemented policies to promote successful transfer between two-year and four-year public institutions, credit loss remains a significant issue for students who transfer or would benefit from learning mobility.^[5]

Unfortunately, improving transfer and learning mobility remains difficult to achieve. Although the field of postsecondary education has improved the collection of data on learner pathways and outcomes, there are limited data available specifically on transfer students. Institutions struggle to use available data to inform decision-making, understand and address barriers to positive learner outcomes, or identify the solutions that can lead to better outcomes for students. For example, a national Transfer Improvement Landscape Scan, conducted by Sova in 2023, revealed extremely limited data and rigorous research related to transfer and learning mobility.^[6] Today’s solutions are not grounded in strong evidence about what works for today’s learners.

This brief is intended to call attention to this ongoing lack of evidence about what works to improve transfer and issue recommendations about how the field can move forward on firmer footing, particularly with regard to students transferring between two-year and four-year institutions. The work needed is at all levels; thus, this brief includes recommendations for state policymakers, institutional leaders, practitioners, and philanthropies.

Build an evidence base by using disaggregated data and conducting rigorous research specific to transfer students, to support and improve outcomes.

A strong data foundation is required for thoughtful policy development, decision-making, and continual improvement. Yet many data systems do not collect or report data specific to transfer students, making it challenging to get a complete understanding of transfer students’ experiences and outcomes.

Improving data on transfer and learning mobility can inform better policy development. Institutions, funders, and state, national, and federal agencies should underwrite more rigorous evaluations of programs, policies, and initiatives aimed at transfer students. For example, a rigorous evaluation of Texas’s Transfer Grant initiative found that providing students with financial support to transfer from a two-year institution to a four-year institution had positive impacts.^[7] States and institutions will be equipped to generate better solutions if they use rigorous, disaggregated evaluation data on transfer students’ mobility, transfer patterns, and outcomes.

Collect data on transfer students specifically, and engage with the data constructively to enhance institutional policies and practices.

Regularly analyzing student outcomes and assessment data can help educators understand who their students are, who has transferred (or sought to transfer) credits earned, how various student groups are faring academically, and what that means for institutional policy and practice.

Senior academic administrators, staff members from units such as the registrar’s office, academic advisers, and faculty members all play roles in determining credit for previous learning. Conversations among these stakeholders should be grounded in a commitment to engaging with the data constructively, by identifying problems, gaps, or improvement opportunities. Those holding these conversations should be mindful that academic advisers and faculty members may reasonably worry that poor outcomes among transfer students could be used to put them in a poor light and, as a result, they may hold back from discussions. They may be less reluctant to do so if data are presented as a way to identify opportunities for improvement and not as a way to critique or threaten practitioners.

Use data to reduce bias in the evaluation of transfer credits.

As noted earlier, credit-evaluation decisions (decisions about whether courses and other prior learning experiences will be applied to program completion) are often made in the dark, without transparent standards or rubrics, rationales for rejection, or open communication back to learners. Instead, many states, systems, and institutions use an ad hoc approach to evaluate credit transfer. This method fails to make use of existing student and institutional data and may allow personal assumptions and biases about student groups, transfer students’ preparation levels, and the quality of institutions they are coming from to influence the credit-evaluation process.

Improving the use of data by those responsible for making these decisions can reduce barriers to successful transfer outcomes. To make such improvements, the following standards could be embedded in this process: (1) existing evidence such as transcripts, course descriptions, and syllabi should inform decisions about granting credit for students’ educational and career progression; (2) justifications for rejecting learning for credit should be required; and (3) when credits are rejected, decision-makers should have to make the case—with evidence including student outcomes data—that a course is not equivalent to their institutions’ offerings and that students who took the course will not be successful.

Invest in quality technology and its implementation to accelerate the use of data in critical learning mobility policies and practices.

When implemented well, technology can help the field address some of the issues described above. At the institution level, credit-technology platforms and applications, such as the Transfer Evaluation System, could be used systematically to ensure that a rationale is provided to learners for each course rejected for credit.^[8]

In addition, multiple tools are under development to ensure students have easy and public access to course equivalencies. Examples include the AI Transfer and Articulation Infrastructure Network (ATAIN) and the Universal Transfer Explorer.^[9] Both efforts were developed to simplify the transfer of credits and provide transparent and up-to-date information to students, advisers, and faculty members. While any newly created systems using artificial intelligence (AI) must be cautious about perpetuating existing biases in credit transfer decision-making, these new AI-generated recommendations offer an opportunity to (1) dramatically increase the number of courses identified as equivalent by institutions, (2) free faculty members and others who conduct credit evaluation to focus on other critical work with students, and (3) reduce the biases introduced by individuals by helping them make recommendations informed by data.

The Joyce Foundation provided support for this issue focus.