The evaluation of undergraduate teaching levels by the Ministry of Education has greatly promoted the construction of software and hardware in universities. After the evaluation, it will be a long-term task to build a long-term decision-making mechanism to guarantee the continuous improvement of teaching quality. As an important part of university education, physical education (PE) has always played an irreplaceable role in improving current students’ physical quality and cultivating their awareness of lifelong physical exercise. How to establish and improve the quality evaluation information system of PE to guarantee the quality of PE is not only related to the awareness of physical education workers about their own teaching work but also to the effective means to regulate the quality monitoring of PE. An effective PE quality evaluation system depends not only on the teaching management department but also on the evaluation of teaching by students and teachers themselves. Only in this way can PE classroom teaching quality be comprehensively and truly reflected. The evaluation of PE quality in higher education is often considered as a multiattribute group decision-making (MAGDM) problem. In this article, the EDAS model is extended to the single-valued neutrosophic sets (SVNSs) setting to deal with MAGDM and the computational steps, for all designs are listed. Finally, the PE quality evaluation is given to prove the SVNN-EDAS model and some good comparative analysis is done to demonstrate the advantages of SVNN-EDAS. It is shown that the SVNN-EDAS method emphasizes the expectation value of the SVNN average alternative. Compared with different methods mentioned, the SVNN-EDAS method is more practical and efficient because the calculation steps are simpler and easier to apply in practice.
With the successful promotion of the new round of basic education curriculum reform, China’s physical education (PE) teaching ideology and PE teaching mode have undergone profound changes, and these changes urgently require schools to establish a PE teaching quality (PETQ) evaluation system that is compatible with them, and urgently resolve the contradiction between theory and practice. The evaluation of teaching quality is not only a value judgment of teachers’ teaching ability and teaching effect, but also a value judgment of students’ learning ability and learning achievement changes. Therefore, it is an important issue of higher education research to construct a university PE teaching quality evaluation system and actively promote the healthy development of university PE teaching evaluation. The PETQ evaluation is viewed as the multi-attribute decision-making (MADM). In order to take the full use of power average (PA) operator and Heronian mean (HM) operator, in this article, we combine the generalized Heronian mean (GHM) operator and PA with 2-tuple linguistic neutrosophic numbers (2TLNNs) to propose the generalized 2-tuple linguistic neutrosophic power weighted HM (G2TLNPWHM) operator. The G2TLNPWHM could relieve the influence of the awkward data through power weights and it could also consider the relationships between the attributes, and it can give more accurate ranking order then the existing methods. The new MADM method is built on G2TLNPWHM operators. Finally, an example for PETQ evaluation in is used to show the proposed methods.
Before the advent of the new era, my country has completed the popularization of higher education, and the country has gradually begun to pay attention to the quality of higher education. Under the background of Healthy China, the quality of physical education teaching in colleges and universities has become a hot topic of social concern. The quality of physical education teaching in colleges and universities directly determines the quality of physical education talents. A reasonable, complete, and scientific evaluation system of physical education teaching quality in colleges and universities is an effective measure to improve the quality of physical education teaching and the training of physical education talents. The physical education teaching quality evaluation in colleges and universities is frequently viewed as the multiple attribute group decision-making (MAGDM) issue. In such paper, taxonomy method is designed for solving the MAGDM under single-valued neutrosophic sets (SVNSs). First, the score function of SVNSs and criteria importance through intercriteria correlation (CRITIC) method is used to derive the attribute weights. Second, then, the optimal choice is obtained through calculating the smallest single-valued neutrosophic number development attribute values from the single-valued neutrosophic number positive ideal solution (SVNNPIS). Finally, a numerical example for physical education teaching quality evaluation in colleges and universities is given to illustrate the built method.
Sports news is a type of discourse that is characterized by a specific vocabulary, style, and tone, and it is typically focused on conveying information about sporting events, athletes, and teams. Thematic context-based deep learning is a powerful approach that can be used to analyze and interpret various forms of natural language, including the discourse expression of sports news. An application model of sign language and lip language recognition based on deep learning is proposed to facilitate people with hearing impairment to easily obtain sports news content. First, the lip language recognition system is constructed; next, MobileNet lightweight network combined with Long-Short Term Memory (LSTM) is used to extract lip reading features. ResNet-50 residual network structure isadopted to extract the features of sign language; finally, the convergence, accuracy, precision and recall of the model are verified respectively. The results show that the loss of training set and test set converges gradually with the increase of iteration times; the lip language recognition model and the sign language recognition model basically tend to be stable after 14 iterations and 12 iterations, respectively, suggesting a better convergence effect of sign language recognition. The accuracy of sign language recognition and lip language recognition is 98.9% and 87.7%, respectively. In sign language recognition, the recognition accuracy of numbers 1, 2, 4, 6 and 8 can reach 100%. In lip language recognition, the recognition accuracy of numbers 2, 3 and 9 is relatively higher. This exploration can facilitate hearing-impaired people to quickly obtain the relevant content in sports news videos, and also provide help for their communication.
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