Construction and prediction of a new learning model based on ARIMA algorithm1

Abstract

At present, in the era of increasingly updated digital tools and resources, the effective design and implementation of learning systems is the key to improving the quality of learning services. The purpose of this paper is to obtain the comprehensive and objective real needs of users through the existing user learning data, make full use of the real-time user online behavior data to obtain the real “ideas” and potential needs of users, and then design the next stage learning system suitable for the designated users. First of all, the article transforms user characteristics into data that can be designed and calculated by collecting information such as behavior big data, and learner information and mapping the relationship between system measurement indicators. Then, the ARIMA time series prediction algorithm is used to predict the user's learning content in the next stage by collecting and calculating the user's characteristics based on the user's historical behavior data. Finally, through the prediction of the user's behavior, complete the design of the user's next stage learning system as a data-driven learning system with output results, and carry out case practice. The research shows that the system calculated and iterated by the ARIMA algorithm has been significantly improved in terms of software efficiency and other aspects, and the data-driven learning system design framework has a significant role in improving the overall needs of users and learning efficiency.