A Mobile-Based Personalized Physical Education Recommendation and Learning Path Optimization Model.
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| Title: | A Mobile-Based Personalized Physical Education Recommendation and Learning Path Optimization Model. |
|---|---|
| Authors: | Zhao, Jianxin1 zhaojianxin@hbfu.edu.cn, Zhu, Hui2 zhuhui3228@163.com |
| Source: | International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 6, p74-88. 15p. |
| Subjects: | Multisensor data fusion, Mobile computing, Physical education students (Education students), Recommender systems, Online education |
| Abstract: | With the advancement of mobile technologies, personalized physical education (PE) has emerged as a critical component of intelligent education. However, existing recommendation models commonly suffer from limited adaptability, insufficient multimodal data integration, and poor alignment between learning paths and learners' real-time states. To address these challenges, this study proposes a three-layer architecture for personalized recommendation and learning path optimization that integrates multi-source perception, dynamic cognition, and real-time optimization. The core innovations of the proposed model include: (1) a lightweight multi-source heterogeneous data fusion module designed for efficient on-device processing; (2) a dynamic tri-state assessment model incorporating skill mastery, fatigue level, and interest level to achieve accurate real-time perception of learners' states; and (3) a duallayer optimization mechanism based on online learning to enable end-cloud collaborative learning path optimization. This study provides a novel technical paradigm for mobile technology-enabled intelligent physical education, offering significant theoretical contributions and practical application value. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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 |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 192680390 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Mobile-Based Personalized Physical Education Recommendation and Learning Path Optimization Model. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Zhao%2C+Jianxin%22">Zhao, Jianxin</searchLink><relatesTo>1</relatesTo><i> zhaojianxin@hbfu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhu%2C+Hui%22">Zhu, Hui</searchLink><relatesTo>2</relatesTo><i> zhuhui3228@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Interactive+Mobile+Technologies%22">International Journal of Interactive Mobile Technologies</searchLink>. 2026, Vol. 20 Issue 6, p74-88. 15p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Multisensor+data+fusion%22">Multisensor data fusion</searchLink><br /><searchLink fieldCode="DE" term="%22Mobile+computing%22">Mobile computing</searchLink><br /><searchLink fieldCode="DE" term="%22Physical+education+students+%28Education+students%29%22">Physical education students (Education students)</searchLink><br /><searchLink fieldCode="DE" term="%22Recommender+systems%22">Recommender systems</searchLink><br /><searchLink fieldCode="DE" term="%22Online+education%22">Online education</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: With the advancement of mobile technologies, personalized physical education (PE) has emerged as a critical component of intelligent education. However, existing recommendation models commonly suffer from limited adaptability, insufficient multimodal data integration, and poor alignment between learning paths and learners' real-time states. To address these challenges, this study proposes a three-layer architecture for personalized recommendation and learning path optimization that integrates multi-source perception, dynamic cognition, and real-time optimization. The core innovations of the proposed model include: (1) a lightweight multi-source heterogeneous data fusion module designed for efficient on-device processing; (2) a dynamic tri-state assessment model incorporating skill mastery, fatigue level, and interest level to achieve accurate real-time perception of learners' states; and (3) a duallayer optimization mechanism based on online learning to enable end-cloud collaborative learning path optimization. This study provides a novel technical paradigm for mobile technology-enabled intelligent physical education, offering significant theoretical contributions and practical application value. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Interactive Mobile Technologies is the property of International Journal of Interactive Mobile Technologies 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192680390 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.3991/ijim.v20i06.60861 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 74 Subjects: – SubjectFull: Multisensor data fusion Type: general – SubjectFull: Mobile computing Type: general – SubjectFull: Physical education students (Education students) Type: general – SubjectFull: Recommender systems Type: general – SubjectFull: Online education Type: general Titles: – TitleFull: A Mobile-Based Personalized Physical Education Recommendation and Learning Path Optimization Model. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Zhao, Jianxin – PersonEntity: Name: NameFull: Zhu, Hui IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 03 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18657923 Numbering: – Type: volume Value: 20 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Interactive Mobile Technologies Type: main |
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