Integrating KEMIRA with Interval-Valued Neutrosophic Numbers to Assess University English Teaching Quality: A Multi-Attribute Decision-Making Model

Evaluating the quality of university English teaching is essential for improving learning outcomes. Key factors include teaching methods, curriculum design, and teacher-student interaction. Effective teachers use engaging approaches that help students develop skills in reading, writing, listening, and speaking. Tailored course content and regular feedback mechanisms, such as surveys and assessments, enhance learning efficiency and address challenges. Teachers' enthusiasm and ability to motivate students greatly influence the quality of teaching. Emphasizing practical communication skills prepares students for real-world applications. This paper introduces the KEmeny Median Indicator Ranks Accordance (KEMIRA) method, combined with interval-valued neutrosophic sets (INSs), to address multiple-attribute decision-making (MADM) problems. A numerical example evaluates the quality of university English teaching, showcasing the advantages of the interval-valued neutrosophic KEMIRA (INN-KEMIRA) approach. Key contributions include extending the KEMIRA model to INSs, determining attribute weights using the average method, and applying INN-KEMIRA to complex MADM problems. A practical case study demonstrates the effectiveness of this approach, with comparative analyses and sensitivity tests validating its accuracy and reliability. This framework provides a robust solution for decision-making under uncertainty, offering valuable insights for educational quality evaluation and similar challenges.