Grey relational analysis for online and offline blended teaching effectiveness evaluation of college English based on triangular fuzzy neutrosophic MADM

Article type: Research Article

Authors: Wang, Xueliang

Affiliations: Department of English Teaching, Huanghe Jiaotong University, Jiaozuo, Henan 454950, China | E-mail: [email protected]

Correspondence: [*] Corresponding author: Department of English Teaching, Huanghe Jiaotong University, Jiaozuo, Henan 454950, China. E-mail: [email protected].

Abstract: The traditional offline classroom is no longer suitable for the current situation of college English classrooms. The application of the “hybrid” teaching model in online and offline college English teaching is the trend and the focus of local higher education reform. Nowadays, many universities at home and abroad have successively established online platforms and launched online courses, striving for seamless connection between online and offline classrooms. It can be seen that exploring the “blended” teaching mode of online and offline in local higher education institutions is a trend of the times and the current situation of English language classrooms in universities. The online and offline blended teaching effectiveness evaluation of college English courses is regarded as multiple-attribute decision-making (MADM). In this paper, the triangular fuzzy neutrosophic numbers grey relational analysis (TFNN-GRA) method is designed for MADM based on the classical point GRA and triangular fuzzy neutrosophic sets (TFNSs). Finally, a numerical example for online and offline blended teaching effectiveness evaluation of college English courses is given and some comparisons are done to show the advantages of TFNN-GRA method.

Keywords: Multiple attribute decision making (MADM), triangular fuzzy neutrosophic sets (TFNSs), grey relational analysis (GRA) method, online and offline blended teaching effectiveness evaluation

DOI: 10.3233/KES-230106

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 27, no. 2, pp. 193-205, 2023