Hundreds of higher education institutions are procuring algorithms that strategically allocate scholarships to convince more students to enroll. In doing so, these enrollment management algorithms help colleges vary the cost of attendance to students’ willingness to pay, a crucial aspect of competition in the higher education market. This paper elaborates on the specific two-stage process by which these algorithms first predict how likely prospective students are to enroll, and second help decide how to disburse scholarships to convince more of those prospective students to attend the college. These algorithms are valuable to colleges for institutional planning and financial stability, as well as to help reach their preferred financial, demographic, and scholastic outcomes for the incoming student body.
Unfortunately, the widespread use of enrollment management algorithms may also be hurting students, especially due to their narrow focus on enrollment. The prevailing evidence suggests that these algorithms generally reduce the amount of scholarship funding offered to students. Further, algorithms excel at identifying a student’s exact willingness to pay, meaning they may drive enrollment while also reducing students’ chances to persist and graduate. The use of this two-step process also opens many subtle channels for algorithmic discrimination to perpetuate unfair financial aid practices. Higher education is already suffering from low graduation rates, high student debt, and stagnant inequality for racial minorities—crises that enrollment algorithms may be making worse.
This paper offers a range of recommendations to ameliorate the risks of enrollment management algorithms in higher education. Categorically, colleges should not use predicted likelihood to enroll in either the admissions process or in awarding need-based aid—these determinations should only be made based on the applicant’s merit and financial circumstances, respectively. When colleges do use algorithms to distribute scholarships, they should proceed cautiously and document their data, processes, and goals. Colleges should also examine how scholarship changes affect students’ likelihood to graduate, or whether they may deepen inequities between student populations. Colleges should also ensure an active role for humans in these processes, such as exclusively using people to evaluate application quality and hiring internal data scientists who can challenge algorithmic specifications. State policymakers should consider the expanding role of these algorithms too, and should try to create more transparency about their use in public institutions. More broadly, policymakers should consider enrollment management algorithms as a concerning symptom of pre-existing trends towards higher tuition, more debt, and reduced accessibility in higher education.
Algorithms have played a role in college enrollment as far back as their use at St. George’s Hospital Medical School in the 1970s—with an algorithm that was later discovered to be discriminating against women and racial minorities. Despite this troubling omen, algorithms have grown continually more important to how colleges shape their incoming student cohorts, a process called enrollment management. According to a 2015 Educause Survey, over 75% of colleges and universities use analytics for enrollment management, up from just over 60% in 2012, making it the most common form of data analytics in higher education. Algorithmic enrollment management is primarily done through vendors, including EAB (serving around 150 institutions), Ruffalo Noel Levitz (or RNL, serving around 300 institutions), Rapid Insight (around 150 institutions), Othot (which lists 30 institutions), Capture Higher Ed (around 100 institutions), Whiteboard Higher Education, and others, although some colleges develop their own algorithms in-house.1 This proliferation of algorithms to at least 700 institutions is not inherently problematic, as colleges have legitimate need to predict the number of students who will attend in a coming year, as well as to budget and prepare accordingly.
“The algorithmic enrollment optimization process warrants additional scrutiny, especially since it may contribute to pre-existing crises in higher education.”
However, there is cause for concern about using algorithms to determine scholarship offers for college applicants, an increasingly common practice. These algorithms help assign scholarships to maximize either net tuition or yield—the percent of accepted applicants who end up attending that specific college. Through a two-part process—first prediction, and then optimization—a college may compare from a handful to thousands of different scholarship disbursement strategies to reach their preferred financial, demographic, and scholastic outcomes for the incoming student body. While these algorithms tend to be effective in increasing net tuition and yield, the most profitable scholarship strategy may not be that which is best for student success. The algorithmic enrollment optimization process warrants additional scrutiny, especially since it may contribute to pre-existing crises in higher education, such as an increase in student debt burdens, higher dropout rates, and the failure of many colleges to proportionately enroll students of color.
Why are colleges turning to enrollment algorithms?
Understanding the broader state of higher education helps illuminate why colleges are turning to algorithms for enrollment management. The yield rate at an average college fell nearly 15 percentage points to 33.7% from 2007 to 2017. This is partially driven by high school graduates applying to more colleges and has led to more furious competition over yielding applicants, including more active recruitment activities. Recruiting one undergraduate student at a four-year private college now has a median cost of around $2,100. As tuition has risen steadily, so too have tuition discounts, such as grants and scholarships. On average, tuition discounts have doubled from $10,000 to $20,000 between 2008 and 2018 and now encompass 52.2% of institutional expenses. This suggests that colleges are more aggressively using both recruitment practices and price variation to entice enrollment.
This competition comes not just from private colleges—public colleges, especially selective, flagship, and land-grant institutions, also compete for the same students. Despite growth over the last eight years, educational support from state and local governments remains lower, on a per-student basis, than the high-water mark in the year 2000. To raise money and compete on educational offerings and amenities, public colleges have raised tuition, including a doubling of in-state tuition (before scholarships), and shifted the proportion of students towards those from out-of-state and other countries—all of whom pay more than in-state…
Read More: Enrollment algorithms are contributing to the crises of higher education