Ranking Universities Based on Career Outcomes of Graduates
Navneet Kapur (GoFundMe), Nikita Lytkin (LinkedIn Corporation), Bee-Chung Chen (LinkedIn Corporation), Deepak Agarwal (LinkedIn Corporation), Igor Perisic (LinkedIn Corporation)
Every year, millions of new students enter higher educational programs. Publicly available rankings of academic programs play a key role in prospective students’ decisions regarding which universities to apply to and enroll in. While surveys indicate that majority of freshmen enter college to get good jobs after graduation, established methodologies for ranking universities rely on indirect indicators of career outcomes such as reputational assessments of the universities among academic peers, acceptance and graduation rates, learning environment, and availability of research funding. In addition, many of these methodologies rely on arbitrary choices of weighting factors for the diﬀerent ranking indicators, and suﬀer from lack of analyses of statistical stability. In this paper, we addresses these challenges holistically by developing a novel methodology for ranking and recommending universities for diﬀerent professions on the basis of career outcomes of professionals who graduated from those schools. Our methodology incorporates a number of techniques for achieving statistical stability, and represents a step towards personalized educational recommendations based on interests and ambitions of individuals. We have applied this methodology on LinkedIn’s Economic Graph data of over 400 million professional from around the world. The resulting university rankings have been made available to the public and demonstrate that there are valuable insights to be gleaned from professional career data on LinkedIn.
Filed under: Graph Mining and Social Networks