Cross-Entropy Method Adapted for Evaluating the Teaching Quality of University Physical Education Utilizing 2-Tuple Linguistic Neutrosophic Sets

Abstract

The evaluation of university physical education teaching quality helps measure 
the actual effectiveness of the courses and ensures the achievement of teaching objectives. 
Through evaluation, schools can identify deficiencies in teaching and make 
improvements, enhancing students' physical fitness and health. Additionally, evaluation 
provides data support for teaching reforms, aiding in curriculum optimization and 
strengthening faculty, thereby promoting the comprehensive development of physical 
education and fostering students' lifelong awareness of fitness and healthy lifestyles.  
The teaching quality evaluation of university physical education adopts the multiple
attribute group decision-making (MAGDM) approach. Currently, cross-entropy (CE) 
model was constructed to handle the MAGDM. To capture uncertain data for the teaching 
quality evaluation of university physical education, 2-tuple linguistic neutrosophic 
numbers (2TLNNs) are administrated as valuable decision tool. This study constructs the 
2TLNN cross-entropy (2TLNN-CE) technique, which combines traditional cross-entropy 
and 2TLNSs. The 2TLNN-CE technique is applied to MAGDM in the context of teaching 
quality evaluation. Furthermore, numerical example for the teaching quality evaluation 
of university physical education is presented, and comparative analysis is administrated 
to validate the advantages of 2TLNN-CE technique. The contributions of this study are 
summarized:(1) Extension of cross-entropy technique to 2TLNSs, enabling its application 
in uncertain decision scenarios; (2) Introduction of 2TLNN-CE technique for MAGDM, 
specifically tailored for teaching quality evaluation; (3) Illustration of 2TLNN-CE 
technique through numerical example of teaching quality evaluation of university 
physical education accompanied by comparative analysis to highlight its benefits. 

DOI: 10.5281/zenodo.14031672