Multi-attribute decision-making methods based on Aczel-Alsina power aggregation operators for English blended teaching quality evaluation under single-valued neutrosophic sets

Article type: Research Article

Authors: Wang, Shuyan

Affiliations: School of Foreign Languages, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China | E-mail: wangshuyan202211@163.com

Correspondence: [*] Corresponding author: School of Foreign Languages, Zhengzhou University of Economics and Business, Zhengzhou, Henan, China. E-mail: wangshuyan202211@163.com.

Abstract: As a new form of teaching, blended teaching can integrate the advantages of classroom teaching and online teaching and improve the overall quality and effectiveness of English teaching under the premise of changing the orientation of teachers and students. The English blended teaching quality evaluation is a multiple attribute decision making (MADM) issue. Aczel-Alsina t-norm and t-conorm operations have the true advantage of changeability by adjusting a decision parameter. power averaging (PA) operator allowed decision attributes to strengthen each other through weight information during fusion, thereby eliminating the influence of subjective weights on the decision results. Motivate through classical Aczel-Alsina operators, PA operator and the single-valued neutrosophic sets (SVNSs), in this paper, the single-valued neutrosophic number Aczel-Alsina power averaging (SVNNAAPA) operator and the single-valued neutrosophic number Aczel-Alsina power weighted averaging (SVNNAAPWA) operator are constructed under SVNSs. The SVNNAAPWA operator is built for MADM. Eventually, an example about English blended teaching quality evaluation and some selected comparative decision analysis is used to depict the SVNNAAPWA method.

Keywords: Multiple attribute decision making (MADM), single-valued neutrosophic sets (SVNSs), power average (PA), English blended teaching quality evaluation

DOI: 10.3233/KES-230069

Journal: International Journal of Knowledge-based and Intelligent Engineering Systems, vol. 28, no. 3, pp. 539-552, 2024