高等教育系统的光明未来:基于“HEPTT模型”的评价与分析

Yu-Liang Chi, Yusi Liu, Minghui Liu
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摘要

在国家发展中,高等教育制度是一个国家努力使其公民在必要的初等和中等教育之外接受进一步教育的重要因素。因此,研究高等教育系统的健康问题具有重要的现实意义。通过对问题的分析,找出了影响我国高等教育体制的一些因素。我们的团队从成本、教育质量、研究水平、政策执行和获取权五个方面研究了这一问题。为了在国家层面上衡量和评估高等教育体系的健康状况,本文开发了一个名为“高等教育暂停和温度测试(HEPTT)模型”的模型。运用该模型,可以反映一个国家高等教育体系的相关情况。本文首先建立了一个主成分分析模型来评价各国高等教育系统的健康状况,并给出了一个综合评分。然后,采用r型聚类分析模型构建神经网络。由此,本文可以对任何一个国家的高等教育体系健康状况的综合得分进行评价。最后,利用感知器算法根据论文期望达到的健康状况来调整各因素的最适应性论文权,从而获得最佳论文权比。此外,本文还利用这一点,为一个有改进空间的国家提供政策建议。
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Bright Future for the Higher Education System: Evaluation and Analysis Based on the “HEPTT Model”
In national development, a system of higher education is an important element in a nation’s efforts to further educate its citizens beyond required primary and secondary education. Therefore, it is valuable to study the health of the higher education system. By analyzing the problem, the paper have found some factors that affect the national higher education system. Our team has studied the problem from five aspects: cost, quality of education, research level, policy enforcement, and access right. To measure and assess the health of a system of higher education at a national level, the paper have developed a model called the “Higher Education Pause and Temperature Test (HEPTT) Model”. By using this model, it can reflect the relevant situation of a nation’s higher education system. The paper first established a Principal Component Analysis Model to evaluate the health of the higher education system in any country and give a comprehensive score. Then, the paper used R-type Cluster Analysis Model to build the neural network. From this, the paper could evaluate the comprehensive score of the health status of any nation’s higher education system. Finally, the paper used the Perceptron Algorithm to adjust the most adaptable the paperight of each factor with the health condition the paper expected to achieve, so as to obtain the best the paperight ratio. Also, the paper used this to provide policy recommendations for a nation with room for improvement.
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