Application of BP Neural Network in Classification and Prediction of Blended Learning Achievements

Abstract

Analyzing and predicting the learning behavior data of students in blended teaching can provide reference basis for teaching. Aiming at weak generalization ability of existing algorithm models in performance prediction, a BP neural network is introduced to classify and predict the grades of students in the blended teaching. L2 regularization term is added to construct the BP neural network model in order to reduce the risk of overfitting. Combined with Pearson coefficient, effective feature data are selected as the validation dataset of the model by mining the data of Chao-Xing platform. The performance of common machine learning algorithms and the BP neural network are compared on the dataset. Experiments show that BP neural network model has stronger generalizability than common machine learning models. The BP neural network with L2 regularization has better fitting ability than the original BP neural network model. It achieves better performance with improved accuracy.