Nia is an eighteen-year-old African American high school graduate from Baltimore. With a 3.7 GPA, Nia aims to pursue an undergraduate program in biotechnology and is waiting for a response to her applications to six universities. Nia requires financial aid (need and merit-based) to make her dreams of going to college a reality. Alie is a Caucasian high school graduate from San Francisco with similar aspirations and GPA. Although Alie has applied for scholarships, her family’s financial standing will permit her to pursue her education in college. Unfortunately, the narrow focus of today’s enrollment management systems is found to reduce the amount of scholarship funding offered to students. So, while Alie sets off to realize her academic goals, Nia’s future could still be uncertain.
Effective estimation of student enrollment and recruitment is critical to the success of the admissions process of any university. The fierce competition among colleges and universities to recruit students is further heightened by the growing tide of online programs spurred by the pandemic. The massive volume of incoming student data can make modern student recruitment a challenging task, especially handling hundreds or thousands of leads. It is imperative for colleges to rely on a lead scoring model to qualify prospective students before diving headfirst into expensive marketing and outreach campaigns. This is where enrollment management systems come in handy.
Over 75% of institutions in the United States use enrollment management systems to drive student recruitment. Algorithmic enrollment management systems help institutions cut costs and time in processing thousands of applications every year. However, there are growing concerns that modern enrollment algorithms are designed to only advance the profit-making objectives of universities, and not necessarily to improve their student body. Further, recent evidence suggests that enrollment algorithms are exacerbating the worrying trends of declining enrollment rates, low graduation rates, soaring student debt, and institutional inequality for racial minorities.
“The total college enrollment in Spring 2022 fell to 16.2 million, a 14.7% decline from Fall 2020” – Education Data Initiative, 2022
Before we evaluate how universities can limit bias and use prevailing technologies responsibly to optimize their enrollment management strategies, it is important to understand the current rationale behind enrollment algorithms.
Typically, enrollment management systems follow a two-pronged approach to student recruitment.
In short, enrollment algorithms pursue objectives outlined by the university. The constraints applied to the algorithm govern the nature of its output. Currently, algorithms are working to maximize values – total student yield and total tuition fee paid – to serve institutions alone rather than bolster their student body. Different constraints can impose demographic diversity, such as race or gender, or even necessitate that a certain mix of prospective applicants enrolls.
It is imperative for institutions to adopt a responsible approach to their enrollment strategy and be mindful of the outcomes they want to achieve while using AI.
Quantiphi is at the forefront of building responsible AI solutions for the education sector. We have leveraged our unique blend of technical and industry expertise, coupled with our steadfast commitment to building a better world with responsible AI to develop an enrollment optimization solution that aligns with the enrollment objectives of institutions in addition to student welfare.
Quantiphi’s lead-scoring model allows universities to input constraints based on their objectives for the incoming student cohort and enables them to target leads based on their propensity scores. The in-house model contributes to significant cost savings compared to those maintained by third-party vendors.
Get in touch with our experts to know how you can optimize your enrollment management strategy.