Abstract
Teaching effect evaluation of College English is frequently considered as a multiattribute group decision-making (MAGDM) issue. Thus, a novel MAGDM method is needed to tackle it. Depending on the classical TOPSIS method and interval-valued intuitionistic fuzzy sets (IVIFSs), this paper designs a novel intuitive distance-based IVIF-TOPSIS method for teaching effect evaluation of College English. First of all, a related literature review is conducted. Furthermore, some necessary theories related to IVIFSs are briefly reviewed. In addition, the weights of attribute are decided objectively by using the CRITIC method. Afterwards, relying on novel distance measures between IVIFSs, the conventional TOPSIS method is extended to the IVIFSs to calculate closeness degree of each alternative from the interval-valued intuitionistic fuzzy positive ideal solution (IVIF-PIS). Finally, an empirical example about teaching effect evaluation of College English and some comparative analyses have been given. The results show that the designed method is useful for teaching effect evaluation of College English.
Highlights
Since the process of making decision is filled with uncertainty and ambiguity [1,2,3,4,5,6,7], in order to cope with the accuracy of decision-making [8,9,10,11,12,13,14], Zadeh [15] defined the fuzzy sets (FSs)
Compute the positive distances d+i between each alternative and IVIF-PIS and the negative distances d−i between each alternative and IVIF-NIS as n d+i rjIVIFEDqNij, A+j , i 1, 2, . . . , m, j 1 n d−i rjIVIFEDqNij, A−j , i 1, 2, . . . , m, j 1 where IVIFED(qNij, A+j ) and IVIFED(qNij, A−j ) denote the IVIF Euclidean distances given in Definition 4, and rj is the weight of attributes
With the development of multimedia technology and the wide use of the Internet and computer, College English teaching is becoming more and more multimodal. e rapid development of information technology promotes the change in the ways of communication and the education idea
Summary
Since the process of making decision is filled with uncertainty and ambiguity [1,2,3,4,5,6,7], in order to cope with the accuracy of decision-making [8,9,10,11,12,13,14], Zadeh [15] defined the fuzzy sets (FSs). Hao et al [28] presented a theory of decision field for IFSs. Gupta et al [29] modified the SIR method and combined it with IFSs. Li et al [30] gave a grey target decision-making with IFNs. Gou et al [31] defined some exponential operational law for IFNs. Khan and Lohani [32] defined similarity measure about IFNs. Bao et al [33] defined prospect theory and evidential reasoning method under IFSs. Oztaysi et al [34] solved the research proposals evaluation for grant funding using IVIFSs. Sahu et al [35] defined the hierarchical clustering of IVIFSs. Xian et al [36] defined combined weighted averaging operator for GDM under IVIFSs. Zhang et al [37] defined the programming technique for MAGDM based on Shapley values and incomplete information. Zhao et al [41] defined the CPT-TODIM method for interval-valued intuitionistic fuzzy MAGDM.
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