Affiliations: [a] Qiongtai Normal University, Haikou, Hainan, China | [b] Information Engineering College, Hangzhou Dianzi University, Hangzhou, Zhejiang, China
Correspondence:
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Corresponding author: Yao Yao, Qiongtai Normal University, Haikou 570000, Hainan, China. E-mail: yaoyao3488@163.com.
Abstract: The mixed online and offline mode is a new teaching mode that is gradually mature and developed based on information technology. Applying this model to English teaching will help teachers get rid of the shackles of traditional models, reconstruct teaching, and improve the flexibility and effectiveness of teaching activities. Analyze the connotation and main characteristics of the mixed online and offline mode, understand students’ English abilities and future development needs, formulate scientific teaching measures that combine online and offline, innovate and optimize teaching activities, create a good learning atmosphere for students, and thereby improve the quality of English teaching. The blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective is viewed as the multiple-attribute decision-making (MADM). In this paper, Taxonomy technique is designed for MADM under interval-valued neutrosophic sets (IVNSs). Then, the interval-valued neutrosophic numbers Taxonomy (IVNN-Taxonomy) technique is formed to cope with MADM problem. Finally, a numerical example for blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective is given to demonstrate the IVNN-Taxonomy technique. The main contribution of this study is: (1) the Taxonomy technique is extended to IVNSs; (2) the entropy technique is employed to implement the weight values based on combined distances; (3) the IVNN-Taxonomy technique is managed to put up with the MADM under IVNSs. (4) a numerical example for blended teaching effectiveness evaluation of ESP courses in universities in an output-oriented perspective has been given. (5) some comparative algorithms are given to show the rationality of IVNN-Taxonomy technique.
