Yueqing Li, Hao Liu, Yong Ge
Education program rankings have long been relied upon by students, parents, and institutions for their ability to assess academic quality and guide educational choices. While rankings significantly influence institutional strategies and student decision-making, traditional ranking systems often lack the nuanced insights needed for truly informed choices. To bridge this gap, this paper introduces an information technology (IT) artifact that enables personalized educational program selection by matching students' career aspirations and skill development goals with relevant program offerings. We develop a three-component architecture comprising: a Wide & Deep skill extractor that identifies labor market skill requirements, CookieLM that generates comprehensive skill representations through language model and knowledge enhancement, and EPRQ that delivers personalized program recommendations aligned with individual career objectives. The empirical analyses demonstrate our framework's superior effectiveness over existing techniques and offer insightful, user-specific evaluations across different scenarios.