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International Journal of Web Applications

Optimization of the Training Effect Mode of the Current Physical Education MOOC System in Universities Guided by K-Means Algorithm

Wang Lei, Chen Xin
College of Chemistry and Chemical Engineering Hunan University of Engineering, Xiangtan City 411100, Hunan Province

Abstract: With the popularity of online learning and MOOCs, sports MOOCs are gradually receiving attention. However, there are some problems with the existing sports MOOC system, such as insufficient training effectiveness and lack of personalization in the learning process. We propose an optimization scheme based on the K-means algorithm to address these issues. This plan first collects studentsâ€™ learning data, including learning duration, practice frequency, discussion participation, etc. Then, we use the K-means algorithm to divide these data into different groups and develop more personalized training plans based on the characteristics and needs of different groups. At the same time, we also considered the characteristics of the sports discipline. We incorporated various factors such as physical health, sports skills, and psychological quality into the training plan to comprehensively improve studentsâ€™ sports literacy. Through experimental verification, we found that the optimization scheme based on the K-means algorithm can significantly improve the training effectiveness of the sports MOOC system. Compared with traditional training modes, the optimized mode has higher learning efficiency and lower learning costs. In addition, students also showed higher satisfaction and stronger learning motivation towards the optimized training mode.

Keywords: K-means algorithm, â€œInternet +â€ background, PE teaching in ordinary universities, MOOC model Optimization of the Training Effect Mode of the Current Physical Education MOOC System in Universities Guided by K-Means Algorithm

DOI:https://doi.org/10.6025/ijwa/2024/16/1/15-23

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