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] |
| Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 187350025 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Characteristics of micro-learning units in college physical education based on improved particle swarm optimization. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Boning%22">Li, Boning</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Zheng%22">Yang, Zheng</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> yangz202404@163.com</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Yunhang%22">Lu, Yunhang</searchLink><relatesTo>3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Intelligent+Decision+Technologies%22">Intelligent Decision Technologies</searchLink>. Jul2025, Vol. 19 Issue 4, p2036-2049. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Microlearning%22">Microlearning</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+education%22">Physical education</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+selection%22">Feature selection</searchLink><br /><searchLink fieldCode="DE" term="%22Cluster+analysis+%28Statistics%29%22">Cluster analysis (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+technology%22">Digital technology</searchLink><br /><searchLink fieldCode="DE" term="%22Effective+teaching%22">Effective teaching</searchLink><br /><searchLink fieldCode="DE" term="%22Particle+swarm+optimization%22">Particle swarm optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Intelligent Decision Technologies is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/18724981251340982 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2036 Subjects: – SubjectFull: Microlearning Type: general – SubjectFull: Physical education Type: general – SubjectFull: Feature selection Type: general – SubjectFull: Cluster analysis (Statistics) Type: general – SubjectFull: Digital technology Type: general – SubjectFull: Effective teaching Type: general – SubjectFull: Particle swarm optimization Type: general Titles: – TitleFull: Characteristics of micro-learning units in college physical education based on improved particle swarm optimization. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Boning – PersonEntity: Name: NameFull: Yang, Zheng – PersonEntity: Name: NameFull: Lu, Yunhang IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 18724981 Numbering: – Type: volume Value: 19 – Type: issue Value: 4 Titles: – TitleFull: Intelligent Decision Technologies Type: main |
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