While institutions of higher education have come to utilize emerging technologies to help facilitate and bolster enrollment trends, the practice of administering enrollment algorithms to allocate scholarships could be worsening the financial stability of prospective students.
In a recent paper by the Brookings Institution, Alex Enger, a governance study fellow, demonstrated the ways in which enrollment management algorithms have contributed to growing higher education crises which include low graduation rates, high student debt, and stagnant inequality for racial minorities.
The purpose of the paper is to explore how algorithms can play a responsible role in higher education enrollment and proposed metrics by which policymakers can aim to ensure that use of the system promotes both the best outcomes for institutions and students alike.
These metrics can be handy for ensuring an institution's financial stability, course availability, and preparing sufficient student housing based on enrollment trends. However these algorithms also excel at identifying a student’s exact willingness to pay, meaning they may drive enrollment while also reducing students’ chances to persist and graduate.
“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,” Enger writes. “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.”
This higher education paper is a part of the Brookings Institution’s Artificial Intelligence and Emerging Technology (AIET) Initiative is part of “AI Governance,” a series that identifies key governance and norm issues related to AI and proposes policy remedies to address the complex challenges associated with emerging technologies.
In order to avert this bifurcated outcome, the paper urges institutions that use algorithms to distribute scholarships to proceed cautiously and document their data, processes, and goals. One way to ensure this is to ensure that there is 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.
In terms of oversight, the paper also calls on state policymakers to consider the expanding role of these algorithms, and to try to “create more transparency about their use in public institutions.”
“More broadly,” Enger writes, “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.”
Publication Date: 9/20/2021