Algorithm Optimization Strategies of the UCAS System: Problem and Countermeasure Analysis Based on the Return of Chinese Overseas Students

Runxi Cui ()
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Runxi Cui: Wenhua ISAL High School

A chapter in Proceedings of the 2025 International Conference on Hybrid Commerce, Human Capital, and Economic Dynamics (ICHCH 2025), 2026, pp 572-581 from Springer

Abstract: Abstract In recent years, due to the implementation of a series of policies by China to attract overseas students to return, more overseas students have chosen to seek employment in their home country. However, during the process of returning to work, overseas students face numerous challenges, such as issues related to academic qualification certification and recognition, information asymmetry in the domestic job market, and mismatches between their skills and the requirements of domestic positions. This article analyzes the core algorithms of the UCAS (Universities and Colleges Admissions Service) system (including score conversion, course matching, competitive admission, and dynamic adjustment algorithms) and compares them with the Chinese admission model centered on college entrance examination scores. The study proposes potential directions for optimizing the UCAS system, such as improving the matching algorithm and developing personalized recommendation modules. However, it also points out that since the UCAS system prioritizes serving local British students, emphasizes the flexibility of university independent admissions and the comprehensive evaluation system, and that algorithm improvements are difficult to fundamentally solve problems such as employer recognition and information deficiency, the feasibility of these reform measures in the UK is relatively low. Ultimately, this article believes that the key to solving the problem of overseas students’ employment in their home country lies with the students themselves, including making good career plans, accumulating relevant domestic experience, and actively breaking through information barriers.

Keywords: UCAS; Algorithms; employment (search for similar items in EconPapers)
Date: 2026
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DOI: 10.2991/978-2-38476-585-0_64

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