Machine-Learning based MOOC learning data analysis

It is an important aspect of the development of online education to record and analyze the educational data generated by online learners, predict the learning effect and provide precise and personalized services for them. In order to realize the prediction learning effect, firstly, the valuable information in the education data is mined and analyzed, and the data modeling is carried out. The data items are defined, and the solution is proposed according to the prediction results. Secondly, K-NN, LVQ and SVM are used to predict the learning effect. The experimental results show that the prediction accuracy of the three algorithms is as high as 70%. Finally, the three models are compared and analyzed, and learning behavior data are combined to provide a decision-making basis for the effective prediction of learning state, which is helpful for the online course platform to better plan students' learning plans and give corresponding learning warnings.