Neutrosophic Soft Rough Sets for Quality Evaluation of Interactive Music Teaching in Higher Education: A Novel Approach

Abstract

 Evaluating interactive teaching effectiveness in higher education presents a significant 
challenge due to the presence of vague, uncertain, and incomplete data derived from diverse 
stakeholders such as students, instructors, and educational technologies. This paper introduces a 
novel decision-making framework based on Neutrosophic Soft Rough Sets (NSRS) to address 
these complexities by integrating power of multiple set theory to effectively handle uncertainty, 
vagueness, and incompleteness in educational assessment scenarios. In Our NSRS model, we 
propose a new aggregation function to fuse expert evaluations across different evaluation 
parameters while preserving uncertainty and parameter importance. Moreover, a novel NSRS 
scoring function is introduced that penalizes both falsity and indeterminacy, which allow 
achieving interpretable rankings of alternatives under uncertainty. We also derived lower and 
upper approximations to categorize alternatives into definite and possible decision regions. For 
validating the practicality of the model in complex academic environments, a comprehensive case 
study on interactive music teaching was lately performed, which involved evaluation of eight 
real-world music courses with respect to five pedagogically pertinent parameters. 

DOI: 10.5281/zenodo.16708762