A Mobile-Based Personalized Physical Education Recommendation and Learning Path Optimization Model.

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Bibliographic Details
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]
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Database:	Engineering Source

Description
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]
ISSN:	18657923
DOI:	10.3991/ijim.v20i06.60861