运用熵权法和层次分析法对不同国家高校水平进行分析

Qing Zhou, Qiang Zhang, Hao Li
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引用次数: 2

摘要

高等教育体系是衡量一个国家教育水平和国力的重要因素。一个健康、可持续的高等教育体系可以有效地提高国家的竞争力。建立完整统一的国家高等教育体系利弊评价模型,是提高高等教育体系水平的基础。在本文中,我们将分四个步骤构建相关模型。第一步是搜索文件和数据,并使用有关多个国家的知识保护、高等教育入学率、教育系统质量、学生互联网接入、政府教育支出、每篇论文被引用次数和学位价值等因素的数据。在此基础上建立一个模型。标准化所有数据。第二步,由于各因素之间可能存在一定的相关性,采用主成分分析法,将等相关变量转化为另一组不相关变量,建立模型。第三步,基于该模型分析加拿大、法国、德国、意大利、日本、俄罗斯、英国、美国等国高等教育体系的健康和可持续性水平,并获得排名和综合得分。第四步是利用熵值法计算各因素的权重。选择一个国家改革最重要的因素。将改造后的数据重新导入模型,观察并分析其变化。
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Analysis on the level of higher universities in different countries using entropy weight method and analytic hierarchy process
The higher education system is an important factor in measuring the education level and national strength of a country. A healthy and sustainable higher education system can effectively improve the country's competitiveness. The establishment of a complete and unified model for evaluating the pros and cons of the national higher education system is the basis for improving the level of the higher education system. In this article, we will build the relevant model in four steps. The first step is to search for documents and data, and use data on factors such as knowledge protection in multiple countries, higher education enrollment rates, education system quality, student Internet access, government education expenditures, the number of citations per paper, and the value of degrees. Build a model based on it. And standardize all data. In the second step, since the factors may have a certain correlation, the principal component analysis method is used to convert the equi-related variables into another set of unrelated variables and establish a model. The third step is to analyze the health and sustainability levels of higher education systems in Canada, France, Germany, Italy, Japan, Russia, the United Kingdom, the United States and other countries based on this model, and obtain rankings and comprehensive scores. The fourth step is to use the entropy method to calculate the specific weight of each factor. Select one of the countries to reform the most weighted factor. Import the reformed data into the model again, observe and analyze its changes.
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