Assessment of Teaching Quality of Traditional Decorative Patterns in Colleges and Universities Using an MCDM Approach and Circular Intuitionistic Information

Evaluating the teaching quality of traditional decorative patterns in colleges and universities is critical, especially when human judgment and subjective opinions are involved. This paper aims to analyze this assessment process using an enhanced framework of circular intuitionistic fuzzy sets (C-IFS) and the Dombi triangular norm. The aim of this research is to apply the Dombi t-norm (DTN) and t-conorm (DTCN) for aggregating the information within the circular intuitionistic fuzzy (C-IF) framework to evaluate the teaching quality of traditional decorative patterns. Another important feature of the proposed study is that prioritization is used in decision-making preferences by using a priority-based weight vector. To serve this purpose, we mainly proposed the Circular Intuitionistic Fuzzy Dombi Prioritized weighted averaging (C-IFDPWA) and Circular Intuitionistic Fuzzy Dombi Prioritized geometric (C-IFDPWG) operators. Moreover, the special cases of proposed aggregation operators (AOs) and their validity are discussed. An intelligent algorithm is provided involving prioritization of attributes and experts based on proposed AOs to evaluate the multi-criteria group decision-making (MCGDM) problem, particularly in assessing teaching quality. A numerical example is included to demonstrate the effectiveness and flexibility of the proposed work, and to validate its application, a comparison analysis has been conducted. The superiority of this model lies in the fact that general structure, less time complexity, and prioritization of factors make it real. The suggested framework prioritizes decision criteria and manages ambiguity well, making it a reliable tool for assessing the quality of instruction. A case study demonstrates its practical relevance and highlights the advantages of applying fuzzy logic to assess the teaching quality of traditional decorative patterns in colleges and universities.