Characteristics of micro-learning units in college physical education based on improved particle swarm optimization.
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| Title: | Characteristics of micro-learning units in college physical education based on improved particle swarm optimization. |
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| Authors: | Li, Boning1 (AUTHOR), Yang, Zheng2 (AUTHOR) yangz202404@163.com, Lu, Yunhang3 (AUTHOR) |
| Source: | Intelligent Decision Technologies. Jul2025, Vol. 19 Issue 4, p2036-2049. 14p. |
| Subjects: | Microlearning, Physical education, Feature selection, Cluster analysis (Statistics), Digital technology, Effective teaching, Particle swarm optimization |
| Abstract: | With the rapid development of new media technology and mobile communication technology, people have entered a "micro" era, from micro-blog and WeChat to micro-store and micro-video, and then developed to micro-classroom related to learning. As one of the important contents in college teaching, physical education classroom is also crucial to build physical education as a learning unit through micro-classroom. However, because of its high dimension and redundancy, micro-learning resources bring adverse effects on clustering, affect the accuracy of clustering, and increase the workload of algorithm calculation and analysis. In view of this, in order to improve the clustering accuracy of micro-learning units in college physical education, this paper selects the features of the clustering data of micro-learning units in college physical education, so as to optimize the feature subset and improve the clustering accuracy of micro-learning units, and provide technical support for improving learners' learning efficiency. Feature selection, as a dimension reduction method, can reduce feature redundancy and noise by selecting representative feature sets for clustering. By fully studying the related theories of micro-learning and feature selection, combined with the characteristics of micro-learning units, Based on the fitness function constructed by mutual information, a feature selection model based on backbone particle swarm optimization algorithm is proposed. The mutation probability in the algorithm is adjusted by using the fitness change information fed back by particle swarm optimization in the iterative process, which can better balance the relationship between global optimization ability and search accuracy. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | With the rapid development of new media technology and mobile communication technology, people have entered a "micro" era, from micro-blog and WeChat to micro-store and micro-video, and then developed to micro-classroom related to learning. As one of the important contents in college teaching, physical education classroom is also crucial to build physical education as a learning unit through micro-classroom. However, because of its high dimension and redundancy, micro-learning resources bring adverse effects on clustering, affect the accuracy of clustering, and increase the workload of algorithm calculation and analysis. In view of this, in order to improve the clustering accuracy of micro-learning units in college physical education, this paper selects the features of the clustering data of micro-learning units in college physical education, so as to optimize the feature subset and improve the clustering accuracy of micro-learning units, and provide technical support for improving learners' learning efficiency. Feature selection, as a dimension reduction method, can reduce feature redundancy and noise by selecting representative feature sets for clustering. By fully studying the related theories of micro-learning and feature selection, combined with the characteristics of micro-learning units, Based on the fitness function constructed by mutual information, a feature selection model based on backbone particle swarm optimization algorithm is proposed. The mutation probability in the algorithm is adjusted by using the fitness change information fed back by particle swarm optimization in the iterative process, which can better balance the relationship between global optimization ability and search accuracy. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 18724981 |
| DOI: | 10.1177/18724981251340982 |
