Abstract
With the continuous deepening of higher education management reform, university leaders have realized that the merger of universities, annual expansion of enrollment, and expansion of educational scale have broadened the development space for universities. At the same time, many management problems have also emerged, and education management problems are particularly prominent, such as some decisions, plans, instructions, etc. of the school level education management department not being well implemented in various departments, and the channels for the school level education management department to understand the true situation of each department are not smooth. Therefore, deepening reform provides a good opportunity for universities to strengthen management and streamline relationships. Teaching and scientific research must be upgraded, and the quality of teaching management must be improved. Establishing an education management quality evaluation system and emphasizing the quality of education management work are the key. The higher education management quality evaluation is affirmed as multi-criteria group decision-making (MCGDM). Interval-valued neutrosophic sets (IVNSs) have been widely used and researched in MCGDM. The interval-valued neutrosophic sets (IVNSs) could depict the uncertain information within the higher education management quality evaluation. The purpose of this article is to proposed a new improved grey relation analysis (GRA) method based on prospect theory (PT-GRA) to solve the MCGDM under IVNSs. At the end of this paper, an example for higher education management quality evaluation is illustrated through the built method and the comparison. Thus, the main contribution of this study is: (1) the PT-GRA method is used to deal with the MCGDM problems under IVNSs; (2) the weight information is obtained through entropy method; (3) an empirical example for higher education management quality evaluation has been given. (4) some comparative algorithms are given to show the rationality of PT-GRA method with IVNSs.
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