An integrated generalized chordal picture fuzzy distance measure with its application to evaluate teaching quality

Abstract

Deepening the integration of industry and education (IIE) in classroom teaching has important strategic significance for the connotative development of higher education. The classroom teaching quality assessment is an important route to improve the level of the IIE and then promote the quality of talent training. Considering that picture fuzzy sets (PFSs) is much more efficient in comparison with fuzzy sets at handling the uncertainty in decision-making problems, this paper firstly applies PFSs to depict the indeterminacy and inaccuracy information in teaching quality assessment (TQA) process. To begin with, the related definitions of PFSs are all profiled successively. Then, we attempt to propose an integrated assessment method with the combined compromise solution (CoCoSo) and Taxonomy method (TM) to handle the TQA problem with PFSs. In this approach, we define a new cumulative method based on TM to aggregate individual opinion into group opinion. We also develop a weighting strategy based on the CRiteria Importance Through Intercriteria Correlation approach (CRITIC) to evaluate the significance of experts with PFSs. For this, we define a novel generalized chordal picture fuzzy (PF) distance measure that considers the marginal impacts of degree of refusal membership, which has a strong capacity of differentiation. Then, we evaluate its superiority and stability through some experiment comparisons. Again, a final ranking method is presented with the CoCoSo approach and the Stepwise Weight Assessment Ratio Analysis (SWARA) method. Furthermore, we apply a case study of the TQA to demonstrate the implementation of the newly proposed PF-CRITIC-SWARA-TM -CoCoSo method. The results obtained from the sensitivity analysis validate that the option “Dr. Tang” consistently achieves the highest rank and is independent of variations of balancing factor and weight information of experts and criteria. Finally, a comparison is implemented to confirm the robustness and reliability of the suggested integrated framework.