Advancements in MADM with college English teaching quality evaluation: Integrating 2-tuple linguistic neutrosophic models with prioritized heronian mean techniques

Abstract

With the continuous development of Internet era, teaching modes are also being innovated and developed, and online and offline blended teaching (OOBT) is one of them, which is mainly achieved by combining OOBT. By combining online and offline teaching modes with college English courses, teachers not only help students cultivate independent learning habits, but also help them think alone and improve their practical skills. This is also true for vocational and technical education, and it can be said that this approach is more relevant. The OOBT evaluation in college English teaching courses is deemed to multiple-attribute decision-making (MADM). In this study, we connected the geometric Heronian mean (GHM) technique and prioritized aggregation (PA) in line with 2-tuple linguistic neutrosophic numbers (2TLNNs) to produce the generalized 2TLNN power GHM (G2TLNPGHM) technique. Then, the G2TLNPGHM technique is implemented to process the MADM under 2TLNNs. Finally, the G2TLNPGHM technique is implemented as the numerical example of evaluating the OOBT in college English courses. Some comparative analysis of the impact of parameters are fully addressed.