Research on Classroom Teaching Quality Assessment Based on AGA—BP Algorithm

Educational informatization has revolutionized classroom teaching quality assessment (CTQA) into an essential tool in order to develop the educational process. The conventional methods for assessment are always driven by the human factor and have the disadvantage of reflecting teaching quality inaccurately. Thus, the current research suggests a hybrid algorithm, AGA-BP, which is the combination of Adaptive Genetic Algorithm (AGA) and Back propagation (BP) neural networks that can be used in the optimization of classroom teaching quality assessment. The use of AGA for the optimization of the network weights, together with BP neural networks, the algorithm accelerates the convergence speed to the optimal solution and reduces the effect of the local optima, thus improving the accuracy of the model's prediction. Experiments done on the network under several datasets indicate that in comparison with BP neural networks, the AGA-BP algorithm is more competent and is efficient in the quality of the assessment of teachers. This approach offers an additional angle and technical support for the educational quality assessment.