Research On Quality Evaluation Of Online And Offline Mixed Teaching Of Advanced Mathematics A Based On Neural Network

In order to implement the "xi general secretary in school ideological and political theory course teacher symposium" on the important speech spirit,system,profound grasp the essence of concept and the scientific method,to adjust measures to local conditions,since,according to their aptitude,actively promote flip classroom,hybrid teaching the new teaching mode,strengthen the affinity of course with pertinence,Strive to create a "golden lesson" that students want to listen to and love to learn.Under this trend,online and offline mixed teaching based on MOOCs has become a new teaching method with obvious advantages.In the process of higher education reform,mixed teaching model combines the advantages of online teaching and traditional teaching,and is gradually applied to the teaching of all kinds of majors.However,there are few research results on how to establish a comprehensive and effective evaluation model of mixed teaching.The autonomy and flexibility of mixed teaching and many links in its implementation process make the establishment of mixed teaching evaluation system more complicated.The traditional teaching quality evaluation system is not suitable for the existing mixed teaching mode.Therefore,this paper establishes a mixed teaching evaluation system according to the implementation process of mixed teaching,and constructs a mixed teaching evaluation model based on BP neural network.Then,aiming at the shortcomings of BP neural network,it proposes to use GA-BP neural network to evaluate the quality of mixed teaching,and carries out an empirical analysis of the two evaluation models.The main work of this paper includes:(1)The evaluation index system of mixed teaching quality has been established.In order to construct the evaluation model of mixed teaching quality,this paper first developed a questionnaire about the evaluation of mixed teaching quality,extracted important indicators based on the three principles and two factors in the paper,and then combined with the content of each scoring item on the platform of the Excellent Course Alliance to get 20 evaluation indicators.A mixed teaching quality evaluation index system is established,which takes teaching evaluation before class,in class and after class as the first level index and extracts 20 items as the second level index.(2)A hybrid teaching evaluation model based on BP neural network is established.Taking the actual situation of the implementation of mixed teaching in a university in Hubei Province as an example,the data were collected and preprocessed.Then the structure of BP neural network is determined according to the established evaluation system,and the data are input for network training,and the evaluation results are obtained.Finally,the error analysis of the results is carried out.(3)A mixed teaching evaluation model based on GA-BP neural network is established.After analyzing the errors of BP evaluation model,in order to optimize the model and improve the evaluation accuracy,GA-BP neural network was proposed to evaluate the quality of mixed teaching.Then determine the structure of the evaluation model,and also input the sample data into the network training to get the evaluation results.Finally,the error analysis of the results is carried out.(4)In order to better reflect and compare the effect of BP and GA-BP neural network evaluation models,the paper continues to input the sample data into the original GA and BSA to get the evaluation results and errors.Then,the two evaluation models of BP and GA-BP are compared with the evaluation results of GA and BSA algorithms.It is found that the evaluation model of GA-BP neural network has a higher accuracy,which can provide a more feasible scheme for the evaluation of mixed teaching quality.