<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Exploring the impact mechanism of online learning effectiveness can effectively optimize and improve the impact to enhance online learning effectiveness. This paper describes a big data Gaussian process regression model under big data technology, and global a priori sparse approximation and local approximation solve the predicted distribution results. The training conditional probabilities and test conditional probabilities of the hidden variables are optimized using the induced point method and the spectral approximation method. Then, a simple analysis of the network learning behavior applied by the ISM model is introduced using the big data Gaussian process regression model, and the key, direct and fundamental factors that may affect the network learning are mined. Finally, relative data analysis was conducted based on the relevant influencing factors mined by the ISM model. From the number of course visits, 34.54% of the learners’ visits to the course are mainly concentrated between 100-199 times. Regarding the length of discussions, the per capita participation in online discussions was 37.32 minutes, and most learners mainly focused on 0-50 minutes. It indicates that using big data Gaussian regression model can analyze the influencing factors of online learning through ISM model, and the targeted improvement and optimization can enhance the effectiveness of online learning.</p>
</abstract>

Exploring the impact mechanism of online learning effectiveness can effectively optimize and improve the impact to enhance online learning effectiveness. This paper describes a big data Gaussian process regression model under big data technology, and global a priori sparse approximation and local approximation solve the predicted distribution results. The training conditional probabilities and test conditional probabilities of the hidden variables are optimized using the induced point method and the spectral approximation method. Then, a simple analysis of the network learning behavior applied by the ISM model is introduced using the big data Gaussian process regression model, and the key, direct and fundamental factors that may affect the network learning are mined. Finally, relative data analysis was conducted based on the relevant influencing factors mined by the ISM model. From the number of course visits, 34.54% of the learners’ visits to the course are mainly concentrated between 100-199 times. Regarding the length of discussions, the per capita participation in online discussions was 37.32 minutes, and most learners mainly focused on 0-50 minutes. It indicates that using big data Gaussian regression model can analyze the influencing factors of online learning through ISM model, and the targeted improvement and optimization can enhance the effectiveness of online learning.

Mechanisms influencing the learning effect of ISM network under big data regression algorithm

Applied Mathematics and Nonlinear Sciences

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

Exploring the impact mechanism of online learning effectiveness can effectively optimize and improve the impact to enhance online learning effectiveness. This...

Mechanisms influencing the learning effect of ISM network under big data regression algorithm

Pubblicato online: 02 set 2023

Pagine: -

Ricevuto: 21 ott 2022

Accettato: 13 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00275

Parole chiave
Gaussian process regression, Induced point method, ISM model, A priori sparse approximation, Network learning effect

© 2023 Guixian Zhang et al., published by Sciendo

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

Mechanisms influencing the learning effect of ISM network under big data regression algorithm

Pubblicato online: 02 set 2023

Pagine: -

Ricevuto: 21 ott 2022

Accettato: 13 mar 2022

DOI: https://doi.org/10.2478/amns.2023.2.00275

Parole chiaveGaussian process regression, Induced point method, ISM model, A priori sparse approximation, Network learning effect

© 2023 Guixian Zhang et al., published by Sciendo

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

Parole chiave
Gaussian process regression, Induced point method, ISM model, A priori sparse approximation, Network learning effect