Yu Daohua, Zhou Xin, Pan Yu, Niu Zhendong, Yuan Xu, Sun Huafei
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100081, China.
School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China.
Entropy (Basel). 2022 Dec 23;25(1):24. doi: 10.3390/e25010024.
With the rapid development of higher education, the evaluation of the academic growth potential of universities has received extensive attention from scholars and educational administrators. Although the number of papers on university academic evaluation is increasing, few scholars have conducted research on the changing trend of university academic performance. Because traditional statistical methods and deep learning techniques have proven to be incapable of handling short time series data well, this paper proposes to adopt topological data analysis (TDA) to extract specified features from short time series data and then construct the model for the prediction of trend of university academic performance. The performance of the proposed method is evaluated by experiments on a real-world university academic performance dataset. By comparing the prediction results given by the Markov chain as well as SVM on the original data and TDA statistics, respectively, we demonstrate that the data generated by TDA methods can help construct very discriminative models and have a great advantage over the traditional models. In addition, this paper gives the prediction results as a reference, which provides a new perspective for the development evaluation of the academic performance of colleges and universities.
随着高等教育的快速发展，高校学术增长潜力评估受到了学者和教育管理者的广泛关注。虽然关于大学学术评价的论文数量在不断增加，但很少有学者对大学学术表现的变化趋势进行研究。由于传统统计方法和深度学习技术已被证明无法很好地处理短时间序列数据，本文提出采用拓扑数据分析（TDA）从短时间序列数据中提取特定特征，进而构建大学学术表现趋势预测模型。通过在真实的大学学术表现数据集上进行实验，对所提方法的性能进行了评估。通过分别比较马尔可夫链以及支持向量机在原始数据和TDA统计数据上给出的预测结果，我们证明了TDA方法生成的数据有助于构建极具判别力的模型，并且相对于传统模型具有很大优势。此外，本文给出预测结果以供参考，为高校学术表现的发展评价提供了新的视角。
