Prediction of learning behavior characters of MOOC's data based on time series analysis

Abstract

In recent years, big data is used in finance, education, biotechnology, and other fields. In the field of education, thanks to the overall characteristics of big data, it can be applied to understand the objective learning rules of students, to provide effective decision-making suggestions for educators. With the development of electronic communication and media technology, Massive Open Online Courses (MOOC), which contains a large number of user behaviors and information, has gradually become one of the mainstream ways of education. However, MOOC have also been criticized for their high dropout rate. Through the analysis of big data contained in MOOC real data set, it can help teachers master students' learning rules and learning behaviors in time, which is of great significance to improve the level of education and the dropout rate of MOOC. First, it is helpful for teachers to adjust the course content in time according to the learning status. Second, teachers can carry out benign interventions on students' learning behavior. In this paper, based on the analysis of the real MOOC data of students' learning behaviors, we find that different groups of students have the same learning behavior at the same learning period, and students' learning situations can be divided into three stages. Then ARIMA model is used to predict the learning behaviors of students, to further analyze the factors causing the difference in prediction accuracy, we propose an error analysis method based on MSE. Through the experimental observation, the method proposed in this paper can effectively find and predict the characteristics of students' learning behavior, to give suggestions for improving the quality of teaching.