A Methodological Approach to Decision-Making Using Interval Valued Neutrosophic Framework for Enhancing and Evaluating English Blended Teaching Quality

Abstract

The future of blended English instruction in universities is promising, driven by ongoing 
technological advancements and the increasing diversity of educational needs. This teaching model 
is expected to better integrate the strengths of both online and in-person learning, providing students 
with more flexible and personalized learning experiences. By combining these two modalities, 
blended instruction can improve student motivation and engagement while allowing educators to 
continuously refine their teaching methods and materials based on real-time data and feedback. As 
a result, blended learning is projected to become a more widespread and preferred method of 
instruction in higher education. In terms of evaluating the quality of blended English teaching, 
multiple-attribute decision-making (MADM) techniques play an important role. Interval-valued 
neutrosophic sets (IVNSs) have been extensively used and studied in MADM for their ability to 
handle uncertainty and imprecision in decision-making processes. In this context, the ORESTE is 
applied under the IVNS framework for ranking the alternatives. This method is particularly useful 
for evaluating the quality of blended English instruction. A numerical example is provided in the 
paper to demonstrate its application in assessing the quality of blended teaching. The study 
concludes that the IVN-ORESTE method not only offers stability but also provides a degree of 
flexibility, which is crucial for adapting to the varying needs and conditions of higher education 
environments. 

DOI: 10.5281/zenodo.14423790