Modeling and simulation of teaching quality in colleges based on BP neural network and training function

Construction of a Prediction Model for Distance Education Quality Assessment Based on Convolutional Neural Network

This paper introduces the principles and operation steps of convolution and pooling of convolutional neural networks in detail. In view of the shortcomings of fixed sampling points and single receptive field in traditional convolution and pooling forms, deformable convolution and deformable pooling are introduced to enhance the network’s ability to adapt to image details and large displacement problems. The concepts of warp, loop optimization, and network stack are introduced. In order to improve the optimization performance of the algorithm, three subnetwork structures and stack models are designed, and various methods are used to improve the prediction accuracy of distance education quality assessment. In order to improve the accuracy and timeliness of education quality assessment, this paper proposes a distance education quality assessment model based on mining algorithms. The prediction index is selected by the improved BP neural network. It is required to establish the input layer node as the input vector based on the number of data sources since the input layer is used for data input. The neural network is trained with a quarter of the mining data, and the mining algorithm is further trained with network error trials. A fuzzy relationship matrix is created based on the assessment of teaching quality’s hierarchical structure. This leads to the conclusion of the fuzzy thorough evaluation of the effectiveness of distant learning. Experiments show that the proposed model has an average accuracy of 96%, the average teaching quality modeling time is 25.44 ms, and the evaluation speed is fast.

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.

Research on the Effectiveness of Probabilistic Stochastic Convolution Neural Network Algorithm in Physical Education Teaching Evaluation

In practice, PE teaching evaluation based on probabilistic convolutional neural network still faces some practical problems. At present, the existing research mainly focuses on how to improve the accuracy of PE (physical education) teaching evaluation, but ignores the balance between accuracy and speed of the model, which is the key to achieve efficient PE teaching estimation. Aiming at the problem of optimization contradiction existing in the traditional probabilistic stochastic convolution neural network regression method, a position adaptive probabilistic stochastic convolution neural network regression method was proposed. Firstly, the basic principle of probabilistic and random convolution neural network regression method is given. Secondly, the contradiction and reasons between hot trial regression and coordinated regression are analyzed. It is found that the process heat will return to the optimization with irreconcilable contradiction with the coordinates due to the lack of learning parameters when the hot trial transforms the coordinates. The optimization contradiction will make the model tonot obtain the exact coordinates of the nodes. Then, based on the above analysis, the learnable parameters are introduced into the Softmax function, and the position adaptive Softmax model is proposed. Combining the model with the probabilistic stochastic convolution neural network regression method, the position adaptive probabilistic stochastic convolution neural network integral regression method is obtained. In order to reduce the training cost of this method, a simplified training strategy is proposed. Finally, the simulation software MATLAB is used for verification, and the functions of sample maintenance, probabilistic stochastic convolution neural network training, and neural network evaluation are realized. The experimental data show that the probabilistic stochastic convolutional neural network is feasible for teaching quality evaluation, meets the accuracy requirements, and indeed provides a convenient and practical tool for PE teaching quality evaluation.

An artificial intelligence data mining technology based evaluation model of education on political and ideological strategy of students

The student’s political and ideological practices is a vital portion of education, and it is related to optimization of task based on fundamental scenario in establishing morality. In order to establish a scientific, reasonable and operable evaluation model for students’ ideological education, and evaluate the status of college students’ ideological education. In this paper, firstly, in view of the shortcomings of evaluation objectives, single evaluation methods, lack of pertinence of evaluation indicators and subjectivity of evaluation standards in the current evaluation system of university students’ ideological and political education, the basic principles for constructing evaluation models of university students’ ideological and political education are put forward. Secondly, in case to meet changing needs of the times, an artificial neural network algorithm based on artificial intelligence data mining and a traditional multi-layer fuzzy evaluation model are designed to evaluate the ideological and political education of college students. This newly proposed model integrates learning, association, recognition, self-adaptive and fuzzy information processing, and at the same time, it overcomes their respective shortcomings. Finally, an example analysis is carried out with a nearby university as an example. The evaluation results display that the evaluation model of students’ ideological education established in this paper is in good agreement with the previous evaluation results. It fully shows that the comprehensive evaluation model of fuzzy neural network for college students’ ideological and political education established in this paper is scientific and effective.