This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Many New York City eighth-graders—particularly those from underserved communities—aren't applying to academically competitive high schools where they could succeed, according to new Cornell research. In partnership with New York City Public Schools (NYCPS), the research team is putting its findings into practice to help combat the phenomenon, known as "undermatching." New York City's public high school application process is one of the nation's largest and most complex, requiring more than 60,000 eighth-graders to navigate hundreds of options.
The system is designed to give students access to schools across the city, regardless of their neighborhood—but that level of choice can quickly become overwhelming, the researchers said. "It's a crazy complex process for how students apply and then match with high schools in New York City," said Nikhil Garg, assistant professor of operations research and information engineering at Cornell Tech and the Cornell Duffield College of Engineering and a co-author of the paper, "Connecting application behavior to undermatching in New York City school choice," published in Nature Cities. "With that flexibility and equality of access comes the challenge of figuring out how to navigate 900 options and decide which ones to apply to." The research was led by Cornell Tech doctoral students Kenny Peng and Emily Ryu and was also co-authored by Jon Kleinberg, the Tisch University Professor of Computer Science and Information Science, and Eva Tardos, the Jacob Gould Schurman Professor of Computer Science, both in Cornell Bowers Computing and Information Science.
The researchers analyzed data from nearly 59,000 students who participated in New York City's high school admissions process during the 2022–23 school year. They looked not only at where students were admitted, but where they could have been admitted had they applied to different schools. By combining admissions data with a model of how students choose schools under uncertainty, the team assessed whether students could have applied to higher-performing programs and where their application behavior may have limited their options.
"Intuitively, we knew that the complexity of the system was a challenge for students and families," Peng said. "But to address this challenge, we thought it was important to first carefully quantify that effect on real data." The researchers found that, on average, New York City students enrolled in schools that admitted about 64% of applicants, even though they could have been admitted to schools that admitted only about 37% of applicants, suggesting many students were not applying to the most competitive schools within reach. This gap was highest for students with the most competitive applications and for Black, Hispanic and lower-income applicants—a finding that raised questions about whether the complexity of the application process itself was driving inequities in student behavior and outcomes, the researchers said.
The study indicates that providing students with more personalized guidance on where they have a realistic chance of admission could help reduce inequities in the admissions process. Discover the latest in science, tech, and space with over 100,000 subscribers who rely on Phys.org for daily insights. d research that matter—daily or weekly. A second paper examines how personalized recommendation systems can help students navigate the high school admissions process.
Led by doctoral student Erica Chiang, who partnered with NYCPS as a Siegal PiTech Ph.D. Impact Fellow at Cornell Tech, the new system developed by researchers and deployed by NYCPS delivered personalized email recommendations to applicants during the 2025–26 admissions cycle. The paper won the Best Paper with Student Lead Author Award at the ACM Conference on Economics and Computation on July 7.
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