Application of an intelligent evaluation model of online teaching based on improved BPNN

Abstract

Online teaching quality evaluation is essential for university management, but the current evaluation model of online teaching quality has problems such as subjectivity, high randomness and slow convergence speed. Therefore, the study uses adaptive learning rate with momentum term for optimizing and improving the traditional back propagation neural network, and the improved adaptive back propagation neural network rating model has good application in small-scale low-dimensional datasets, and its computational power is weak in large-scale datasets. Therefore, the study uses deep noise reduction autoencoder to improve the model structure, and then obtains an improved backpropagation neural network. The model shows better performance in a self-made university online teaching evaluation dataset. When the quantity of hidden layers of the improved model is 2, the mean square error value of the model is 0.0026; when the quantity of hidden layer neurons is 25, the mean square error of the model is 0.0012; when the model is made comparison with the shallow model, the mean absolute percentage error, the mean square error, and the root mean square error perform the best with the values of 0.0502, 24.53, and 4.95, respectively. the experimental results verify that the experiment verified that the improved model possesses powerful computational ability, and also proved that it also possesses excellent forecasting convergence, which can better help universities to complete the evaluation of online teaching quality.