KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Sumettikoon, PiyapongSubject: search.filters.subject.Neural network

Search Results

Now showing 1 - 2 of 2
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    INTELLIGENT RECOGNITION OF PHYSICAL EDUCATION CURRICULUM RESOURCES BASED ON DEEP NEURAL NETWORK AND THE GAME MODEL STUDY
    (2023-01-01)
    Yang, Xuelin
    ;
    Sumettikoon, Piyapong
    ;
    Wu, Xiang
    Nowadays, withmore and more physical education curriculum resources, schools or teachers have more and more choices for physical education curriculum resources. However, because some teachers need a deep understanding of curriculum training programs and standards, the selected curriculum resources cannot promote their curriculum development. This paper puts forward the researchon the intelligent recognition and gamemodelof physical education curriculum resources based on neural networks. The specific research conclusions are as follows: The intelligent consciousness and movement model of physical education curriculum resources based entirely on the technical knowledge of the BP neural community and deepneuralcommunityare proposed. WiththehelpofMATLAB7.1 neuralnetwork toolbox to implement the specificrecommendation system, a three-layerBPnetwork is established, and the NEWFF function is used to create the neural network. Useful resources in each direction generate a directionrecognition vector according to the route guidancestandard, calculate the course recommendation degree according to selection statistics and scoring, and input the courseresourcerecognitionvector and recommendationdegreeinto theneuralnetwork. When the number of hidden layer nodes is 10, and the learning training algorithm selects the L-M optimization algorithm, the error between the actual output and the expected output of the network meets the requirements. It shows that the accuracy of the recommendation model meets the requirements; that is, the relationship between the recognition vector of physical education course resources and the recommendation degree of course resources reflected by the neural network basically reflects the functional relationship between them and the model can be used to make corresponding recommendations.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    An Advanced Mixed Methodology Model for Teaching of Physical Education in the Post Covid-19 Era: A Case Study on Junior Middle School Basketball Class
    (2022-01-01)
    Yang, Xuelin
    ;
    Sumettikoon, Piyapong
    Objective: The study's primary purpose is to examine the effectiveness of physical education instruction post-covid-19, focusing on the efficacy of basketball instruction in enhancing teacher-student interaction. Methodology: The research utilized an advanced mixture methodology. Compared to traditional blended instruction, our method considers the students' demand for basketball courses and the post-COVID-19 learning environment. Results: In the post-COVID-19 era, our hybrid online and offline teaching method may be effective in middle school physical education, as demonstrated by our analysis and test case. Students are more satisfied with this method of instruction, and their enthusiasm for acquiring physical education knowledge increases, resulting in enhanced physical education in junior high schools. We examine basketball training for junior high school students and propose a hybrid online/offline training approach to improve effectiveness and satisfaction. Our hybrid teaching model, ADDIE, is designed to accomplish a combination of online and offline instruction and utilizes a neural network to detect errors in students' feedback videos and increase the efficiency of identifying wrong actions. In the final stage of case verification, experimental results indicate that the students' satisfaction with our method is 8.9, showing its potential to meet the requirements of junior middle school physical education instruction after COVID-19. Implications: The study will aid policymakers and educators in formulating post-covid-19 policies regarding the efficacy of physical education instruction. Novelty: The study is among the first to examine the efficacy of physical education instruction in the post-covid era.