Personalized Recommendation Method of Sports Online Video Teaching Resources Based on Multiuser Characteristics

This paper first proposes the teaching model of college sports smart classrooms based on an online teaching platform and analyzes its basic structure and teaching process. Secondly, a gray correlation degree model is constructed for the teaching evaluation of the physical education SCT model, and the comparative sequence and reference sequence of the gray system are used to realize the solution of the correlation degree. Finally, the teaching quality evaluation index system of the physical education SCT model was constructed with SD University College of Physical Education, and the effectiveness of the application of physical education SCT model was analyzed. The results showed that the correlation degrees of a teacher teaching, student learning, environmental resources, and curriculum development were 0.317, 0.229, 0.225, and 0.266, respectively, and the physical education SCT model should focus on teaching methods and other aspects of consideration.

Section: Literature Reviewmentioning

confidence: 99%

Intelligent physical education teaching system in colleges and universities based on grey correlation degree model

Wei,

2023

“…The sports teaching evaluation system's construction can serve as a reference for developing the sports teaching model in colleges and universities based on empirical data. Literature [18] determined the components of each clustering point using the k-means clustering algorithm and iteratively completed the data collection. With the help of cosine similarity, the similar data in the data segments were removed, and the data of video segments with high similarity in the online video teaching resources of sports were removed to get further data normalization.…”

Section: Related Researchmentioning

confidence: 99%

Constructing a Guarantee System for Physical Education Teaching Reform and Innovation in the Digital Era

Yao

2023

In this paper, the world coordinate system transformation in virtual reality technology is utilized to obtain the gesture information for estimating human movement in the process of physical education, and the dynamic sampling light projection algorithm is used to match the gestures of human sports movements. The selection algorithm based on the Q statistic defines the virtual reality technology difference as the error trend of the existence of the difference obtained by each classifier for the new data sports students, and at the same time, combines with the VRT method to construct the VR sports teaching model. In the digital teaching of sports, digital sports teaching materials can be produced by motion editing and video synthesis, and statistical analysis methods can be used to study and analyze the teaching of sports in the digital era. The results show that the highest value recognized by the supervised training algorithm based on JPCD+SRD is 0.92, while the highest value recognized by this paper’s algorithm is 1, i.e., it shows that the effect obtained by this paper’s algorithm is higher than that of supervised training methods, which makes the effect of action recognition in virtual reality technology further improved. The average scores of sit-ups of girls in the control class and the experimental class were improved by 4.03 points and 11.4 points, respectively, compared with those before the experiment, i.e., the use of Internet tools to assist teaching has a significant promotion effect on the sit-up scores of girls. This study facilitates teachers to innovate teaching methods and promotes the process of digital teaching reform in physical education.

“…In order to share teaching resources more accurately, combining the teaching resources knowledge graph context processing method with the dual behavior aggregation method, the personalized teaching resources recommendation model, DB-CGAT model, is proposed, which integrally takes into account the learner's characteristic information, the structural context information in the resource graph, and the historical behavioral information in the behavioral graph, and ultimately realizes the multi-dimensional preference personalized teaching resources sharing [22][23].…”

Section: Personalized Sharing Model Of Teaching Resources Incorporati...mentioning

confidence: 99%

Research on the Design of Digital Teaching Resource Management and Sharing Platform in Colleges and Universities Based on Cloud Storage

Wang

2023

In this paper, after analyzing the structural system of the digital resource sharing model in colleges and universities, a shared storage model based on cloud storage is designed, and a digital teaching resource management and sharing platform for colleges and universities based on cloud storage is constructed on this basis. In order to realize accurate sharing, the sharing behaviors of teaching resources are symbolically illustrated, double behavior aggregation is performed, and a multidimensional preference personalized recommendation algorithm, i.e., DB-CGAT model, is designed to construct the personalized sharing module of teaching resources that integrates the behavioral graph. In addition, three types of experiments are conducted on the personalized sharing module of the platform to test its sharing performance and overall performance. The results show that the accuracy of this model is reduced by 0.04 compared with the traditional algorithm; the 90 percent time of login transaction is 2.44, and the time of query transaction is 1.46, which is all up to the standard value. All the action actions are green, and the number of clicks and throughputs in 5~30ss are around 120. Universities can benefit from the digital teaching resource management and sharing platform designed in this paper, which can improve teaching efficiency.