Analysis And Research On MOOC Learning Behaviors Based On Learning Analysis

With the growing maturity of computer technology,the popularity of the Internet,the development of learning analysis technology and the coming of the era of big data,online learning has become a popular learning mode in the world.Among all kinds of network learning,MOOC learning is developing rapidly,with largest influence and widest range of influence.But there are still some problems which have been criticized by others,such as high dropout rate and poor learning effect.The coming of the era of large data and the development of learning analysis have brought about the possibility of solving these problems.This paper studies and summaries through the learning analysis and network learning behavior of domestic and foreign literature,determines the research model and framework,and uses a variety of tools and learning analysis technology to analyze the learning behavior data from MOOC course "the History and Culture of Taiwan"from three aspects which includes the resource utilization behaviors,work behaviors,self-testing behaviors and the forum behaviors in order to find out the Characteristics of learning behaviors,differences in learning behavior,and the effect of personal characteristics on learning behaviors.The results shows that the learner is insufficient in the MOOC study,the students'learning behaviors differs a lot,and personal characteristics will affect the learning behavior of learners.The students can clearly express their views in the discussion area,but the interaction is rare.Based on the analysis,this paper predicts wheher the students will drop out based on the learning behaviors,and the accuracy is up to 86.38%.To find out the impact of learning behavior on learning effect from macroscopic to microscopic,several learning analysis technology is used including multiple regression equation.By this way we can not only find the essential learning behaviors which has great influence on learning effect,but also can help predict for the next grade in the course.