我国高等教育质量影响因素分析

发布时间：2018-07-01 10:13

本文选题：高等教育质量 + 主成分分析　；参考：《中南大学》2013年硕士论文

【摘要】：摘要：当前,高等教育发展的内外部条件都发生了巨大的变化,持续提高高等教育质量是高等教育改革与发展的主旋律。高等教育关于到我国的人才储备,如何保证高等教育质量不断提升,研究影响高等教育质量的主要因素以及它们的影响程度显得十分重要。在以往的高等教育质量研究中,多数学者都是定性分析,没有或者很少进行定量分析,很难形成一种科学系统的高等教育质量控制研究方法。鉴于此,本文尝试从数学的角度对我国高等教育质量进行量化分析。本文首先在大量的文献阅读和分析的基础上,整合出了影响我国高等教育质量的五大类资源(办学经费、办学设施、师资力量、学生质量和科研条件),用主成分分析将教育所需五大类资源的主要因素进行指标筛选得到了十个关键主成分,然后对这些关键主成分做了两种系统聚类分析,研究各个地区之间的差异；用SVM技术对我国高等教育质量的影响因素问题进行了回归分析,从机器学习的角度提出了一个泛化能力较强的回归模型,得出了高等教育质量影响因素的拟合函数以及各个影响因素的大小。在本文的最后通过全文定性与定量相结合的分析,提出了如何利用现有资源来提高我国高等教育质量的政策性建议。本文共有图9幅,表23个,参考文献64篇。
[Abstract]:Absrtact: at present, great changes have taken place in the internal and external conditions of the development of higher education, and continuous improvement of the quality of higher education is the main melody of the reform and development of higher education. It is very important to study the main factors that affect the quality of higher education and the degree of their influence on the quality of higher education. In the past research on the quality of higher education, most of the scholars are qualitative analysis, there is no or few quantitative analysis, it is difficult to form a scientific system of higher education quality control research method. In view of this, this paper attempts to analyze the quality of higher education in China quantitatively from the point of view of mathematics. On the basis of reading and analyzing a large number of documents, this paper integrates five kinds of resources (running funds, running facilities, teaching staff, etc.) that affect the quality of higher education in our country. By using principal component analysis (PCA), ten key principal components were obtained by selecting the main factors of the five kinds of resources needed for education, and then two kinds of systematic cluster analysis were made for these key principal components. This paper studies the differences between different regions, makes regression analysis on the factors affecting the quality of higher education in China by using SVM technology, and puts forward a regression model with strong generalization ability from the point of view of machine learning. The fitting function of the influencing factors of higher education quality and the size of each influencing factor are obtained. At the end of this paper, through the combination of qualitative and quantitative analysis, the author puts forward some policy suggestions on how to use the existing resources to improve the quality of higher education in China. There are 9 figures, 23 tables and 64 references in this paper.
【学位授予单位】：中南大学
【学位级别】：硕士
【学位授予年份】：2013
【分类号】：G649.2

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