Enhanced Techniques for Multi-Attribute Decision-Making Based on Neutrosophic Model

Abstract

The quality of teaching in university law courses is difficult to assess using traditional average-rating approaches because legal education involves not only doctrinal accuracy, interpretive and argumentation skills, and ethical awareness, but also dialogue and fair assessment. This paper proposes a context-oriented neutrosophic a multiple attribute decision making framework for assessing teaching quality in university law courses. It represents alternatives through degrees of truth, indeterminacy, and falsity; incorporates expert confidence into the aggregation procedure; and employs a parameterized divergence-based closeness measure to distinguish alternatives that appear similar under traditional weighted scores. Methodologically, the proposed approach integrates uncertainty representation with a context-dependent divergence-based penalty to avoid favoring inconsistent assessments while preserving meaningful pedagogical information. In particular, the framework is illustrated through the assessment of five university law teaching profiles based on nine criteria.