New AI/ML Technology Helps Schools Pivot Their Student Recruitment Strategies
by: ElectrifAi and Education IT Reporter
June 23, 2020
In the wake of the COVID-19 pandemic, higher education institutions are now facing increasing challenges to attract students and grow student enrollment. This challenge has been magnified by the financial crisis with a large number of students reluctant to begin or return to school this fall, especially as concerns with growing unemployment spike record numbers.
Colleges and universities face unprecedented challenges to ensure full classes of qualified and promising students, and traditional recruitment tactics are proving ineffective.
Institutions must find a better way to define their target student and optimize enrollment in a more competitive market. Fortunately, artificial intelligence (AI) and machine learning (ML) is offering higher education an innovative way to do this more effectively.
Momentum building to drop test-based admissions scores
Up until recently, schools have used SAT/ACT/GMAT/GRE scores as a key measure of qualification for admission. But even before the pandemic hit, there was much debate over the efficacy of how these scores could predict academic success or even career potential. Now, with online classes and virtual testing adding another layer of uncertainty to this process, major university systems like California have dropped this requirement given concerns about fairness.
Many more schools are considering dropping test scores entirely and several institutions, including the University of Chicago, the University of Rochester, and Marquette University, have already moved to a test-optional policy to help attract a broader range of college applicants.
The University of Rochester, which generally receives a high number of applicants, found that having a “test flexible” period made it evident that test scores added little value to the admissions decision process. Marquette chose to drop the requirements for test scores as part of a campaign to attract a more diverse student pool.
The impact of the COVID-19 on student enrollment
While numerous higher education institutions have worked for years to compete more effectively given declining enrollment trends, the COVID-19 pandemic has been a catalyst for change. Lower enrollment is forecasted this fall as a result of financial disruption, social distancing policies, and concerns with shift to online learning.
Many universities also must face loss of international students given latest restrictions. Essentially, pent up concerns that have been building for years regarding the admissions process for higher education are being exasperated by COVID-19, paired with travel and social distancing restrictions – we’ve created the perfect catalyst for a year of enrollment unlike any before.
Admissions departments will face increasing challenges and disruption given these structural and macro issues in higher education, resulting in a cascade effect across the system covering Tier 1, 2, 3, down through the community colleges.
We will have to learn new ways to make admissions decisions in the face of uncertainty and increased competition by other schools, of which will require largely different approaches for large state schools, regional universities, or small private colleges.
AI/ML provides sharper insights into student profiles
As the cost of operations continues to increase and student enrollments decline, AI/ML can provide institutions with better insights to understand ideal student targets and fit within the higher education landscape. The admission process is fairly subjective. AI/ML can help admissions team make more objective decisions that improve student outcomes. For example, imagine you have to evaluate thousand of incoming student applications.
It’s quite easy to identify the top-ranking students and rule out those at the bottom tier for admission. But how do you account for all the qualities that may prove either group successful, or objectively evaluate the large population that’s in the middle. Many have similar backgrounds, scores, etc. but AI/ML can help better predict which students will be most successful and which ones are more interested in your university.
AI/ML can paint a complete picture of a student and enable admissions departments to more effective and efficient. But there’s no one size fits all model for a university or specific programs. AI/ML algorithms can look more broadly across a broad data set and be tailored to predict outcomes by each unique education program.
AI/ML can evaluate thousands of combinations and reveal new insights that help admissions team make better decisions, ensuring a diverse and promising incoming class that will likely achieve success beyond graduation. Looking forward, universities must apply increased precision and sophistication to compete more effectively and acquire quality students that fit the brand profile. Armed with the right AI/ML solution, schools can effectively mine through applications and support enrollment and financial goals of the university.
Recent months have propositioned higher education institutions with challenges unlike any other in history, but it also has ushered in an opportunity to rethink the admissions process. By equipping these institutions with technologies that are already proving monumental for every other industry, we can create better practices and more opportunity for generations to come.