Paper by Alex Engler: “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….(More)”.