Application of the Big Data Grey Relational Decision-Making Algorithm to the Evaluation of Resource Utilization in Higher Education

Application of the Big Data Grey Relational Decision-Making Algorithm to the Evaluation of Resource Utilization in Higher Education

Ji Huan (School of Jincheng, Nanjing University of Aeronautics and Astronautics, Nanjing, China) and Ren Bo (Jilin University, Changchun, China)
Copyright: © 2018 |Pages: 13
DOI: 10.4018/IJEIS.2018040103

Abstract

In this article, the authors apply the big grey relational decision-making algorithm to improve performance evaluation effectiveness of the higher educational resources utilization. First, they discuss the performance evaluation indexes in higher education. Second, they propose the big data grey relational decision algorithm. Third, they establish the mathematical models of entropy weight and grey evaluation method. Finally, the authors carry out an evaluation simulation analysis on four cities as researching objects. The results show that the big data grey relational decision-making algorithm is an effective method for evaluating the higher educational resource utilization.
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2. Literature Review

The economic benefit of input and output of higher education resources cannot be quantified. For a long time, the higher education has been regarded as consumption and welfare, so it has only investment and no output. The university as nonprofit organization has no power and pressure of improving education resources utility. The problem of higher education resources utility has not been formed the systematic theory, and there is no specific evaluation index system. The current evaluation index system of economic performance concludes sales profit margin, rate of return on total assets, capital yield, ratio of capital accumulation, social contribution rate, social accumulation rate and so on (Ahed and Louis, 2010; Cheng & Zhou, 2015; Tian et al., 2017). These evaluation indexes are confirmed considering the corporate investors, creditors and corporate contribution to society, which are suitable for enterprise. The university is no-material production sector, and fruits of labor are talents and science and technology. It is difficult be measured in money because the labor fruits of it is not represented as material goods. Therefore, the higher education resources utility can be completely evaluated based on quantitative index (John, 2016; Xu and Li, 2007).

The educational process of higher education needs to invest a certain amount of living labor and materialized labor that appears as a certain amount of labor, material and financial resources. The higher education process can produce the educational results with a certain quality and quantity. Suppose that improving students' knowledge, ability and comprehensive quality is viewed as output of higher education, and then the educational output can be described by a certain quality and quantity of students.

Grey relational decision-making algorithm has been successfully applied in many fields in recent years. Gao, Yang & Luo, Junzhou (2009) applied the grey relational decision-making algorithm to carry out information security risk assessment. Ke et al. (2007) put forward the multiple criteria decision-making algorithm based on similarity to ideal grey relational projection. A parking lot optimal routing is designed based on grey entropy relation grade multi-attribute decision making (Zhang et al., 2015). The performance measurement model of knowledge management is constructed based on grey relational analysis (Yang et al., 2015).

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