Method for Evaluating the Effectiveness of Physical Education Classes Utilizing offline and Online Mobile Edge Computing

Abstract

Convergence is occurring between the two fast-evolving domains of computing at the edge and artificial intelligence. The synergy between these two technologies seems like it may lead to even better results. In physical education (PE), neither students nor instructors are tied to a single place, and neither the curriculum nor the classes repeat themselves. That's why it's hard for conventional colleges to evaluate their PE courses accurately. This article uses an optimization model created for edge computing to improve the conventional way of assessing the efficacy of PE programs utilized by most schools. The goal of physical education programs in traditional schools is to improve students' overall health and well-being. Consequently, the ratio of final exam scores is given less weight in this teacher evaluation model than the proportion of students who fulfill the requirements for a physical fitness assessment. The study aimed to develop measures for evaluating the efficacy of kinesiology programs in two-year institutions. This approach enables continuous self-assessment of learning and provides immediate responses to teacher effectiveness. One method that might be utilized to fine-tune the setup is edge computing. Based on the findings of the experiments conducted, it seems that the PES designed for this research has the potential to gather student feedback and enhance the scholarly character of PE teaching objectives. The PES for use in inclusive universities presented in this study is a prime example of this. The edge-computation optimization strategy created for this architecture has the ability to decrease the network's transference amount of the total system by 20 percent while simultaneously improving efficiency by 15%.