Construction of Online Teaching Mode of College Sports Based on Neural Network Technology

Abstract

Online teaching is becoming familiar in recent days for bridging the educational gap and long-distance coverage. The online teaching modes provide better visualization and representation of different subjects, including sports and physical education classes. Data-to-representation provides a better teaching model without interrupting the knowledge-based performance. For leveraging this feature, this article introduces a Conveyance-dependent Teaching Mode (CTM) paradigm. The proposed paradigm employs neural learning to improve the performance of different online sports sessions. This learning assigns rewards for different training modes based on student assessment and data representation. The flawless data and student performance assessments are balanced for improving teaching modes. The reward is updated using the sports curriculum, teacher recommendations, and student interests. Based on the assessment, the rewards are identified using the activation function. This generates the rewards for different sports training sessions improving the data accuracy. Further metrics such as representation, analysis rate, and time factors are verified to improve the performance of the proposed paradigm.

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