The evaluation of teaching quality is a complicated and fuzzy nonlinear process involving numerous aspects and variables, making it difficult to construct a mathematical model, and the old approach of assessing teaching quality is no longer entirely competent. An enhanced GA-BPNN technique based on genetic algorithm (GA) and backpropagation neural network (BPNN) is suggested to evaluate teaching quality effectively and precisely. To begin, an index system for evaluating teaching quality is constructed, and a questionnaire is designed to gather data using the index system. The model parameters are then optimised to create an English teaching quality evaluation system. The GA-BPNN method has an average assessment accuracy of 98.56 percent, which is 13.23 percent greater than the BPNN model and 5.85 percent higher than the optimised BPNN model, according to the simulation. The comparative findings suggest that the GA-BPNN algorithm may produce fair and scientific results when evaluating teaching quality. The GA-BPNN strategy described in this paper searches locally near the global optimal solution, successfully overcoming the slow convergence speed of the conventional approach as well as the problem of being easily local limited to the minimum.
Author(s) Details:Yaowu Zhu,
Editorial Office of the Journal, Anhui Vocational College of City Management, Hefei 230011, China and Institute of Psychology CAS, Beijing 100101, China.
Please see the link here: https://stm.bookpi.org/CRLLE-V5/article/view/6717
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