Data-Driven Curriculum and Curriculum Design in Management and Physical Education Science

Abstract

With the rapid deployment of smart wearable devices and wireless sensing technologies, multimodal physiological and behavioral data have become increasingly available for intelligent educational applications. This study investigates a data-driven curriculum design framework for management and physical education science by integrating learning analytics with wearable sensing and adaptive decision-making mechanisms. A multidimensional data acquisition architecture is established to collect academic performance, behavioral trajectories, and biometric information from smart textile sensors, while standardized data processing and visualization strategies are employed to support evidence-based curriculum optimization. The proposed framework further incorporates teacher data literacy enhancement and a closed-loop feedback mechanism to continuously refine instructional objectives, content organization, and evaluation strategies. By combining real-time monitoring with quantitative learning assessment, the framework enables dynamic adjustment of teaching intensity and personalized intervention according to learner characteristics. In addition, structured data governance and cross-departmental collaboration improve the consistency and reliability of curriculum management. Rather than relying solely on traditional experience-based instructional design, the proposed methodology establishes an adaptive information-processing architecture that integrates multimodal sensing, continuous feedback, and intelligent decision support. This framework provides valuable engineering references for wearable electromagnetic sensing systems, wireless educational monitoring platforms, distributed information acquisition, and human-centered communication networks in next-generation smart learning environments.