Decision Algorithm of Teaching Quality Evaluation for Higher Education System Based on Intuitionistic Fuzzy Geometric Yager Heronian Mean Operators

A reliable and data-based teaching quality evaluation is essential for the continuous improvement of higher-education systems. However, the inherent ambiguity of assessment indicators and the subjectivity of evaluators render traditional, crisp-value models insufficient. To address this challenge, we develop a novel intuitionistic fuzzy multi-attribute decision-making framework that integrates Yager triangular norms (t-norms) with the geometric Heronian mean. Specifically, we first introduce intuitionistic fuzzy operations based on Yager t-norms and Yager t-conorms and subsequently construct two aggregation operators: the intuitionistic fuzzy geometric Heronian mean operators and the intuitionistic fuzzy weighted geometric Heronian mean operators. The idempotency, monotonicity, and boundedness properties of these operators are formally proven. Next, the intuitionistic fuzzy weighted geometric Heronian mean operators are employed to develop an approach for multi-attribute decision-making in classroom teaching quality evaluation under intuitionistic fuzzy information. Moreover, an application case study of teaching quality evaluation in an intuitionistic fuzzy environment is presented to demonstrate the practicality and effectiveness of the proposed approach. Additionally, sensitivity and comparative analyses with other techniques are carried out to further confirm the coherence and superiority of the recommended approach. The research results clearly show that our proposed method is highly effective in accurately evaluating teaching quality and can serve as a valuable tool for educational institutions in enhancing their teaching quality management.