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<p>To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.</p>
</abstract>

To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to enhance the teaching effectiveness of jurisprudence courses. Regression modeling deals with the correlation between teaching variables and establishes a linear regression model in the design process. The sample likelihood function is constructed to output the teaching regression coefficients and the metrics of the model fitting effect, the raw data are dimensionless, and the dimensionless model covariance is calculated according to the nature of teaching expectations. On this basis, the digital teaching model of the jurisprudence course was explored to optimize digital teaching resources, adjust the structure, and establish a student evaluation mechanism. To verify the feasibility of this teaching model, its teaching effect was tested. The results showed that the digital teaching model of jurisprudence based on linear regression led to a teaching level of 2.6594%, and students’ mastery of knowledge reached 2.7952%. And the number of students with failing grades decreased by 480%, and the number of students receiving excellent grades increased by 3.53 times, with a teaching quality rating range of [3.95-4.39] points. It can be seen that the linear regression model promoted the jurisprudence course to break through the traditional teaching model and further improve the teaching quality of the jurisprudence course.

A digital teaching model for jurisprudence courses based on a linear regression model

Applied Mathematics and Nonlinear Sciences

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

To cultivate highly qualified legal talents, this paper designs a digital teaching model for jurisprudence courses based on a linear regression model to...

A digital teaching model for jurisprudence courses based on a linear regression model

Online veröffentlicht: 19. Juli 2023

Seitenbereich: -

Eingereicht: 21. Apr. 2022

Akzeptiert: 17. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00058

Schlüsselwörter
Linear regression model, Digital teaching model, Sample likelihood function, Fitting effect, Dimensionless treatment

© 2023 Jing Luo, published by Sciendo

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

A digital teaching model for jurisprudence courses based on a linear regression model

Online veröffentlicht: 19. Juli 2023

Seitenbereich: -

Eingereicht: 21. Apr. 2022

Akzeptiert: 17. Nov. 2022

DOI: https://doi.org/10.2478/amns.2023.2.00058

SchlüsselwörterLinear regression model, Digital teaching model, Sample likelihood function, Fitting effect, Dimensionless treatment

© 2023 Jing Luo, published by Sciendo

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

Schlüsselwörter
Linear regression model, Digital teaching model, Sample likelihood function, Fitting effect, Dimensionless treatment