School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
School of International Studies, Zhejiang University, Hangzhou, Zhejiang, China.
PLoS One. 2022 Oct 28;17(10):e0274221. doi: 10.1371/journal.pone.0274221. eCollection 2022.
Predicting the admission scores of colleges and universities is significant for high school graduates in the College Entrance Examination in China (which is also called "Gaokao" for short). The practice of parallel application for the students after Gaokao not only puts forward a question about how students could make the best of their scores and make the best choice, but also results in the strong competition among different colleges and universities, with the institutions all striving to admit high-performing students in this examination. However, existing prevailing prediction algorithms and models of the admission score of the colleges and universities based on machine learning methods do not take such competitive relationship into consideration, but simply make predictions for individual college or university, causing low predication accuracy and poor generalization capability. This paper intends to analyze such competitive relationship by extracting the important features (e.g., project, location and score discrepancy) of colleges and universities. A novel competition model incorporating the coarse clustering is thus proposed to make the predictions for colleges and universities in a same cluster. By using Gaokao data of Shanxi province in China from 2016 to 2019, we testify the proposed model in comparison with several benchmark methods. The experimental results show that the precision within the error of 3 points and 5 points are 7.3% and 2.8% higher respectively than the second-best algorithm. It has proven that the competition model has the capability to fit the competitive relationship, thus improving the predication accuracy to a large extent. Theoretically, the method proposed could provide a more advanced and comprehensive view about the analysis of factors that may influence the admission score of higher institutions. Practically, the model proposed with high accuracy could help the students make the best of their scores and apply for the college and universities more scientifically.
预测高校录取分数线对中国高考（简称“高考”）的高中毕业生具有重要意义。高考后学生平行志愿的做法，不仅提出了学生如何充分利用分数、做出最佳选择的问题，而且导致了不同高校之间的激烈竞争，各高校都在争取在这次考试中录取高分学生。然而，现有的基于机器学习方法的高校录取分数线预测算法和模型并没有考虑到这种竞争关系，而是简单地对个别高校进行预测，导致预测精度低，泛化能力差。本文旨在通过提取高校的重要特征（如项目、位置和分数差异）来分析这种竞争关系。因此，提出了一种新的竞争模型，该模型结合了粗聚类，以便对同一聚类中的高校进行预测。利用中国山西省 2016 年至 2019 年的高考数据，我们将提出的模型与几个基准方法进行了比较验证。实验结果表明，在误差为 3 分和 5 分的精度分别比第二好的算法高 7.3%和 2.8%。事实证明，竞争模型能够拟合竞争关系，从而在很大程度上提高了预测精度。从理论上讲，该方法可以提供一个更先进、更全面的视角来分析可能影响高校录取分数线的因素。从实践上讲，该模型具有高精度，可以帮助学生更好地利用分数，更科学地申请高校。
