A Robust and Uncertainty Neutrosophic-Fuzzy MCDM for Analying the Teaching Quality of Agricultural College English

Abstract

The informational leadership of college instructors for English teaching is becoming increasingly important for modern schooling, but its evaluation is somewhat non-rigorous due to the unclear indicator system and the unrestrained expert appraisal. To this end, a new neutrosophic modeling method combining fuzzy expression and MCDM is proposed to establish a rigorous indicator system and determine the key factors. It consists of three parts: establishing and verifying the indicator system; coding experts' comments into the neutrosophic-fuzzy format to express their support, rejection, and indeterminacy of the indicators (and multiple hesitant values); and applying a correlation-based MCDM approach for ranking against the ideal. One complete case study, including stepwise computations, a comparative study with the conventional fuzzy MCDM, and a sensitivity analysis, is presented. The results produce a stable ranking with acceptable perturbations and provide transparent core indicators and driving factors for education decision-makers.