Optimization Construction of Online Physical Education Instructional Platform in Universities Based on Education Big Data

As a new form of university physical education (PE), online PE instructional platform has the advantages of convenience, flexibility and interaction. However, in practical application, there are still some problems, such as the low utilization rate of curriculum resources and the unsatisfactory learning effect of students. Through the collection, analysis and utilization of educational big data (BD), we can deeply understand students’ learning behavior and needs, and provide strong support for individualized recommendation system. This study attempts to apply educational BD to the optimization of online PE instructional platform, aiming at improving the utilization efficiency and learning effect of curriculum resources. By applying individualized recommendation algorithm of PE curriculum resources based on educational BD, online PE instructional platform can better understand students’ learning behavior and needs. The results show that the algorithm is superior to the traditional resource acquisition methods in accuracy and recall. Moreover, the recommended content has been well evaluated by students. Therefore, the algorithm can provide new methods for the optimization of online PE instructional platform, and promote the growth of educational informationization and intelligence.