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<p>The application of virtual practice teaching to the teaching of college Civics and Political Science class helps to develop a new way of practical teaching in ideological and political theory classes. This paper constructs a virtual practice teaching platform through the Ridge regression model, first calculates the least squares estimation of the virtual practice teaching model and sets up the matrix, standardizes the matrix for the original data set, and gets the estimation cluster of Ridge regression. Then the random variables under virtual practice teaching are given by variance expansion factors, the functions of the practice teaching matrix are defined, and the teaching time modeling analysis is performed with Ridge regression to derive the operating parameters of the matrix. Finally, the innovation path of virtual practice teaching is derived based on the constructed platform parameters. The simulation results show that the head-up rate of students under the virtual practice teaching class for freshmen students reaches 97.54% and 95.14% for sophomores, which is 25.13% and 13.84% higher than that of traditional classrooms. Thus, it can be seen that the platform constructed in this paper is conducive to applying the virtual practice to the teaching mode of college Civics class, promoting the innovative path of college Civics class, and improving the communication and communication ability of students.</p>
</abstract>

The application of virtual practice teaching to the teaching of college Civics and Political Science class helps to develop a new way of practical teaching in ideological and political theory classes. This paper constructs a virtual practice teaching platform through the Ridge regression model, first calculates the least squares estimation of the virtual practice teaching model and sets up the matrix, standardizes the matrix for the original data set, and gets the estimation cluster of Ridge regression. Then the random variables under virtual practice teaching are given by variance expansion factors, the functions of the practice teaching matrix are defined, and the teaching time modeling analysis is performed with Ridge regression to derive the operating parameters of the matrix. Finally, the innovation path of virtual practice teaching is derived based on the constructed platform parameters. The simulation results show that the head-up rate of students under the virtual practice teaching class for freshmen students reaches 97.54% and 95.14% for sophomores, which is 25.13% and 13.84% higher than that of traditional classrooms. Thus, it can be seen that the platform constructed in this paper is conducive to applying the virtual practice to the teaching mode of college Civics class, promoting the innovative path of college Civics class, and improving the communication and communication ability of students.

The innovation path of virtual practice teaching in college Civics class based on the Ridge regression model

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

The application of virtual practice teaching to the teaching of college Civics and Political Science class helps to develop a new way of practical teaching in...

The innovation path of virtual practice teaching in college Civics class based on the Ridge regression model

Pubblicato online: 09 giu 2023

Pagine: -

Ricevuto: 05 ago 2022

Accettato: 15 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00377

Parole chiave
Ridge regression model, Least squares estimation, Random variables, Data set, Variance expansion factor

© 2023 Fengzhi Han et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

The innovation path of virtual practice teaching in college Civics class based on the Ridge regression model

Pubblicato online: 09 giu 2023

Pagine: -

Ricevuto: 05 ago 2022

Accettato: 15 dic 2022

DOI: https://doi.org/10.2478/amns.2023.1.00377

Parole chiaveRidge regression model, Least squares estimation, Random variables, Data set, Variance expansion factor

© 2023 Fengzhi Han et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
Ridge regression model, Least squares estimation, Random variables, Data set, Variance expansion factor