How enrollment algorithms are making the student debt crisis worse
Scholar advocates regulating the use of enrollment management algorithms in higher education.
The average rate of return among freshmen enrolling in U.S. colleges decreases by more than 10 percentage points between 2007 and 2017. These declines guest public universities to compete with very selective establishments to recruit students.
Simultaneously, state and local funding for higher education continues decreases. As a result, many public universities altered their admissions practices by implementing new recruitment strategies and tuition variation to incentivize students to enroll.
An admissions innovation implemented by universities in recent years involves the use of enrollment management algorithms. Using these algorithms can reduce the amount of scholarships awarded to students who need them the most, according to a data science expert Alex Enger in a recent Brookings Institution report.
Schools have more and more used algorithms during admissions cycles to increase yield and meet tuition-based revenue goals. In fact, the majority of universities participating in a 2015 survey reported use artificial intelligence (AI) to optimize registration management. These schools hire independent vendors to develop predictive models to help them determine the likelihood of a student enrolling if admitted.
According to Engler, the use of these algorithms should be scrutinized because they threaten to compound pre-existing financial crises in the higher education sector. Universities without large endowments face enormous pressure to produce enough students to cover institutional expenses without giving too much scholarship aid, often while weighing other goals, such as attracting diverse and well-rounded students. Recent data reveal that the use of enrollment algorithms can cause schools to prioritize performance and scholarship optimization over their other goals.
Stock market optimization has traditionally been conducted manually, allowing financial aid officers to combine the use of predictive algorithms with human selection mechanisms to account for factors such as student diversity. However, growing trends signal that more and more schools are moving towards pure algorithmic optimization, which eliminates human selection from the process.
The only recourse to the algorithmic management of registrations could results placing too much emphasis on metrics such as test scores and engagement in pre-college interactions with a given school. Therefore, algorithmic biases can discriminate against students of color and those from modest families by reducing the amount of aid granted to them.
For example, the use of predictive modeling in the admissions process decreases the workload of financial aid offices, allow schools to prepare enough student accommodation, and assists administrators to ensure course availability. Additionally, some vendors pin up that their algorithms will help institutions dramatically increase student enrollment and increase revenue through scholarship optimization, which could Translate millions of additional dollars in tuition revenue for a school in a given year.
The benefits of pure algorithmic optimization are numerous, observed Engler. A recent study find that the use of algorithmic scholarship optimization has significantly increased the enrollment of foreign applicants in a major public university. Another study simulated that this approach would significantly increase yield. Moreover, at least one provider has implemented this type of algorithm, allowing its customers to increase enrollment while minimizing the amount of scholarships awarded to students. With such obvious benefits for universities, the market is likely to see a proliferation of AI in enrollment management in the coming years, Engler suggests.
Engler argues that decision makers should demand transparency from both vendors providing enrollment management algorithms and the universities that employ them. The federal government could accomplish that, Engler suggests, by creating an independent commission to study the use of AI by universities in the admissions and registration process. He also has recommended that the U.S. Congress encourage data sharing between universities and providers as a way to inform future policymaking.
Engler also argues that the US Department of Education should use its regulatory authority under the new Office of Management and Budget advice federal agencies on how and when to regulate the use of AI in the private sector. The Ministry of Education should to look for issue corresponding guidance to institutions on best practices and pursue enforcement action against those who use enrollment management systems irresponsibly, Engler urges.
Finally, Engler recommended that policymakers consider why so many higher education institutions have increasingly turned to enrollment management algorithms. He suggests that the government must improve college access and affordability by providing better funding for higher education and forcing schools to reduce tuition fees. Universities will then feel less pressure to engage AI to optimize scholarship offers as a way to protect performance at the expense of students with the greatest financial need.
The European Commission has already recognized the high-risk nature of registration management algorithms and later released proposed AI regulations restricting their use. Advocates and scholars who agree with Engler urge the United States to follow suit.