A New Entropy-influence-aware Interval-Valued Neutrosophic Bonferroni Aggregation and its Application to Academic Teaching Evaluation of University Staff during Educational Reforms

Abstract

 Assessing academic teaching has become increasingly critical to educational 
reforms, which emphasize innovation, learner-centered approaches, and digital 
integration. All these processes suffer from uncertainty, subjectivity, and indeterminacy 
inherent in expert judgments.  As a remedy, we propose a novel neutrosophic aggregation 
operator called Entropy-Influence Aware Interval-Valued Neutrosophic Bonferroni 
Aggregation (EW-IA-IVNS-BA). This method is grounded in the theory of Interval
Valued Neutrosophic Sets (IVNS), which offers a powerful framework for modeling 
imprecise and hesitant information. Our operator computes entropy-based weights and 
influence indices to enable the proposed method to account for both the variability and 
contextual importance of each evaluation criterion. Then, the weighted Bonferroni mean 
is calculated to capture interdependencies among expert opinions while ensuring interval 
consistency. A real-world-inspired case study involving academic staff performance 
demonstrates the effectiveness and practicality of the method. Quantitative as well as 
qualitative results highlight its potential as a rigorous and interpretable neutrosophic tool 
for educational quality assessment under uncertainty. 

DOI: 10.5281/zenodo.16500278