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

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.

Keywords

• gaussian process regression
• induced point method
• ism model
• a priori sparse approximation
• network learning effect
• 68t05