Prediction of the Improvement Effect of Visualization Technology on Online Learning from the Perspective of the Neural Network

Abstract

In today’s world, data visualization is employed in every aspect of life, and online course makers should take use of the wealth of behavioral data provided by students. Currently, data visualization is being used to suit the development needs of online education in the Internet age. It is also a strong assurance for the online course platform’s improvement and implementation. Data visualization is already closely related to our lives. For online education, the application of data visualization can help course builders understand learners’ learning time characteristics, learning behavior habits, and learning improvement effects, so as to provide learners with corresponding learning guidance, solve learners’ learning difficulties, and improve learning efficiency and course teaching quality. In order to confirm the improvement effect of visualization technology on online learning, the following work is done in this study. This study describes the current state of visualization technology in the United States and internationally, as well as the foundation for the prediction approach that will be proposed later. There are many factors in the evaluation of the online learning effect, and it is dynamic, which is a nonlinear manifestation. The nonlinear computing, self-learning, and high fault endurance of artificial neural network technology are used in this article, and an online learning effect improvement prediction model based on the improved BP neural network is established, namely, the Levenberg–Marquardt back propagation (LMBP) prediction model. The experimental results suggest that the model has a good level of accuracy and may be used to forecast the effect of online learning improvement.