Research on Optimization of Online Auxiliary Teaching Model for College Students’ Physical Education Based on Reinforcement Learning: A Case Study of “Ledong Space APP”
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Abstract
The rapid advancement of digital technologies has created new opportunities for transforming college physical education through the integration of reinforcement learning, smart wearable systems, and intelligent sensing technologies. As wireless communication infrastructures and electromagnetic information transmission increasingly support real-time interaction between wearable devices and mobile platforms, adaptive sports education has become an important application scenario for data-driven learning environments. This study integrates reinforcement learning algorithms with mobile application technology to construct the Ledong Space APP auxiliary teaching system. The proposed framework collects students’ exercise data through a state perception module, dynamically adjusts training difficulty and instructional content using Q-learning algorithms, and employs personalized reward mechanisms to encourage sustained participation. A quasi-experimental study involving three universities in South China compared an experimental group (n = 1,050) using the APP with a control group (n = 1,050) receiving conventional instruction. The results indicate that the experimental group achieved a 37.8% greater improvement in skill assessment scores, increased classroom participation by 41.2%, and reached an 86.4% satisfaction rate with personalized teaching. By integrating smart wearable sensing and adaptive reinforcement learning strategies, the proposed model overcomes the temporal and spatial limitations of traditional physical education while enabling precise motion monitoring and individualized feedback. The framework further demonstrates the potential of intelligent sensing and electromagnetic communication technologies to support scalable, data-driven physical education systems and personalized athletic training.
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