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<p>At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are becoming more and more prominent. This paper firstly analyzes the discrete regression model, and after comparing the logit model under random utility selection and its three derivative models, this paper selects the most widely used hybrid logit model to evaluate and analyze the online teaching platform of university public sports tennis with the MNL model of the maximum likelihood method for error analysis. Secondly, the overall architecture of the online teaching platform system is proposed, and the online teaching platform student satisfaction is studied and analyzed. Finally, based on the hybrid logit model, the AIC, BIC, and log-likelihood value indexes of the online teaching platform for college public education tennis are analyzed for the goodness of fit. The results of the study showed that students were more concerned about three aspects of the online teaching platform: operational difficulty, course comprehensiveness, and learning efficiency, which were 65%, 69%, and 74%, respectively. The AIC and BIC indicators of the hybrid logit model were 4753.656 and 4901.321, which were 192.142 and 117.848 lower than those of the conditional logit model, respectively, indicating that the model fits well and provides a better evaluation and analysis of the platform.</p>
</abstract>

At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are becoming more and more prominent. This paper firstly analyzes the discrete regression model, and after comparing the logit model under random utility selection and its three derivative models, this paper selects the most widely used hybrid logit model to evaluate and analyze the online teaching platform of university public sports tennis with the MNL model of the maximum likelihood method for error analysis. Secondly, the overall architecture of the online teaching platform system is proposed, and the online teaching platform student satisfaction is studied and analyzed. Finally, based on the hybrid logit model, the AIC, BIC, and log-likelihood value indexes of the online teaching platform for college public education tennis are analyzed for the goodness of fit. The results of the study showed that students were more concerned about three aspects of the online teaching platform: operational difficulty, course comprehensiveness, and learning efficiency, which were 65%, 69%, and 74%, respectively. The AIC and BIC indicators of the hybrid logit model were 4753.656 and 4901.321, which were 192.142 and 117.848 lower than those of the conditional logit model, respectively, indicating that the model fits well and provides a better evaluation and analysis of the platform.

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Department of Physical Education and Research

Applied Mathematics and Nonlinear Sciences

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

At present, online teaching is becoming an increasingly popular teaching activity, and while it is gaining more and more popularity, its quality issues are...

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Online veröffentlicht: 04. Okt. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 14. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00500

Schlüsselwörter
Discrete regression model, Logit model, Maximum likelihood method, Online teaching platform, AIC index

© 2023 Xu Jiang et al., published by Sciendo

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

Design and application of an online teaching platform for public sports tennis in colleges and universities based on discrete choice regression model

Online veröffentlicht: 04. Okt. 2023

Seitenbereich: -

Eingereicht: 18. Nov. 2022

Akzeptiert: 14. Apr. 2023

DOI: https://doi.org/10.2478/amns.2023.2.00500

SchlüsselwörterDiscrete regression model, Logit model, Maximum likelihood method, Online teaching platform, AIC index

© 2023 Xu Jiang et al., published by Sciendo

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

Schlüsselwörter
Discrete regression model, Logit model, Maximum likelihood method, Online teaching platform, AIC index