Modified EDAS method for spherical fuzzy multiple attribute group decision making and applications to English classroom teaching quality evaluation

Author:

Hu Guanghua1

Affiliation:

1. Zhengzhou University of Economics and Business, Zhengzhou, Henan, P.R. China

Abstract

Classroom teaching is an important link related to the quality of teaching and talent cultivation. In the implementation of classroom teaching, we should fully attach importance to the main position of students, the role of educational technology and information technology in teaching activities, make use of the latest educational ideas and educational concepts, and combine the actual situation of college English teaching and college English teaching in China, and attach importance to foreign language teaching theories and practices at home and abroad, Establish monitoring indicators and monitoring system for college English teaching quality. Under the guidance of effective monitoring indicators, teachers’ teaching concepts can be updated and improved in real time to achieve better teaching results. At the same time, the quality assurance and monitoring system of college English teaching can be continuously improved to make it more perfect. The English classroom teaching quality evaluation could be deemed as a classic multiple attribute group decision making (MAGDM) problem. Spherical fuzzy sets (SFSs) can excavate the uncertainty and fuzziness in MAGDM more effectively and deeply. This article we first present a novel score function to compare spherical fuzzy numbers (SFNs) more directly and efficiently. Then, on basis of evaluation based on distance from average solution (EDAS), a novel spherical fuzzy EDAS (SF-EDAS) method is built for dealing with MAGDM. Moreover, when the attribute weights are completely unknown, the MEthod based on the Removal Effects of Criteria (MEREC) is extended to spherical fuzzy environment (SFE) to reasonably acquire the attribute weights. Finally, SF-EDAS approach is used for English classroom teaching quality evaluation to prove practicability of the developed method and compare SF-EDAS method with existing methods to further demonstrate its legitimacy and superiority.

Publisher

IOS Press

Subject

Artificial Intelligence,General Engineering,Statistics and Probability

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