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

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.