Research on blended online and offline teaching methods for physical education courses based on fuzzy neural networks

Blended learning typically depends on online platforms and tools for educational delivery, which may lead to decreased student interest and engagement in courses.To enhance student participation and improve performance outcomes in blended learning environments, this study investigates the online and offline (On&Of) blended teaching model for physical education courses using fuzzy neural networks.Initially, On&Of learning data from physical education classes is collected, and the data is preprocessed using the GROUP BY syntax for aggregation.Next, collaborative filtering algorithms are employed to extract key learning features from the On&Of physical education courses.These features are then clustered using fuzzy clustering algorithms and association rules to categorize the learning characteristics of students.Finally, a fuzzy neural network model is developed to integrate the On&Of teaching data for physical education.Based on the fusion of these data sets, specific teaching activities are designed to create an optimized blended learning method for physical education classes.After conducting experimental verification, it is demonstrated that the proposed blended On&Of teaching method leads to an improvement in student grades by more than 20%, with participation rates consistently exceeding 88%.These results indicate that the approach significantly enhances the learning outcomes in physical education courses, showing positive effects for its application.