A Factorization Deep Product Neural Network for Student Physical Performance Prediction

As we all know, sports have great benefits for students. However, with more and more learning pressure, students' physical education has not been paid attention to by teachers and parents, so the analysis and prediction of physical education performance have become significant work. This paper proposes a new method (factorization deep product neural network) for PE course score prediction. The experimental results show that, compared with the existing performance prediction methods (LR, SVM, FM, and the DNN), the proposed method achieves the best prediction effect on the sports education dataset. Compared with the traditional optimal methods, the accuracy and AUC of DNN are both improved by 2%. In addition, there is also a significant improvement in accuracy, recall, and F1. In addition, this study found that considering two or more features at the same time has a certain influence on the prediction results of students' grades. The proposed feature combination method can learn feature combinations automatically, consider the influence of first-order features, second-order features, and high-order features in the meantime, and acquire the relationship information between each feature and performance. Compared with single-feature learning, the proposed method in this paper can enhance prediction accuracy significantly. Moreover, several dimensionality reduction methods are used in this paper, and we found that the PCA model for data processing outperformed all the benchmark models.