Adaptive Neutrosophic Consensus Aggregation (ANCA) for Evaluating Teaching Quality in University Dance Programs Neutrosophic Modeling

Abstract

Evaluating teaching quality in university dance programs is intrinsically complex due to 
multidimensional criteria, diverse evaluator groups, and the coexistence of uncertainty, 
contradiction, and missing information. This paper proposes Adaptive Neutrosophic 
Consensus Aggregation (ANCA) a decision framework that represents individual 
judgments as neutrosophic triplets (T,I,F) and aggregates them via a single-parameter 
consensus operator. Truth components are combined using a conciliatory power mean, 
falsity via a cautious power mean, while indeterminacy integrates (i) self-reported 
hesitation, (ii) robust group disagreement, and (iii) missingness. The framework yields a 
normalized utility score U∈[0,1]  that is interpretable, fair, and robust. A case study with 
two instructors and four criteria (artistic mastery, pedagogy, creativity, and engagement) 
demonstrates that ANCA provides transparent rankings, criterion-level insights, and an 
auditable consensus gate to trigger re-discussion when evaluator disagreement is high. 
The approach advances neutrosophic decision-making in education and offers a practical, 
accountable method for quality assurance in performing-arts contexts.

DOI: 10.5281/zenodo.16934612