A Method for Evaluating the Quality of Mathematics Education Based on Artificial Neural Network

Teaching quality evaluation (TQE) is an important link in the process of school teaching management. Evaluation indicators and teaching quality have a complicated and nonlinear connection that is influenced by several variables. Some of these drawbacks include too much subjectivity and unpredictability, difficulty in defining index weights, sluggish convergence, and weak computer capacity. The current assessment techniques and models have these issues as well. This research uses an ANN model to assess the quality of mathematics instruction at colleges and universities (CAU) in order to address the challenging nonlinear issue of TQE and completes the following tasks. (1) The background and significance of TQE research are analyzed, and the domestic and foreign research status of TQE and neural network is systematically expounded. (2) The technical principle of DNN is introduced and the DDAE-SVR DNN model is constructed, and then, the evaluation index system of mathematics teaching quality is constructed. (3) The DDAE-SVR DNN model is put out as a potential alternative. The Adam method is used in the unsupervised training process to dynamically modify the learning step size for each training parameter. The spatial properties of the original data may be modified several times such that the reconstruction can be completed after many hidden layers have been applied. Data essentials such as precision, accuracy, and consistency are prioritized above all other considerations when generating the final product. Unsupervised prediction uses SVR and maps the complicated nonlinear connection into high-dimensional space in order to attain linearity in low-dimensional space, which is the goal of the supervised prediction process. The usefulness and benefits of the model provided in this research in the mathematics TQE may be verified by tests including the use of the TQE dataset and comparisons with other shallow models.