## 4 Discussion  
In previous work, some scholars used traditional classification methods such as logical regression LR [5; 6; 7], KNN [6], and SVM [7; 8; 9] to establish prediction models. Some scholars also use neural network methods such as CNN [3; 10; 11] and LSTM [11; 12] to predict dropout behavior. Although the above methods have achieved good results in predicting the dropout rate, they have not statistically analyzed and visualized the factors affecting MOOC learners' dropout behavior. Some other scholars theoretically analyzed the strategies to maintain MOOC learning motivation [13] and explored the impact of forum posting and social network behavior on dropout [15]. However, their work focus on the theoretical analysis of the influencing factors of MOOC dropout, lacking a quantitative analysis of each factor. This paper selects gender, age, source country, academic qualification, active time, video playing time, course cumulative days, and the number of forum posts to analyze learners' dropout behavior, from the two aspects of learners' own characteristics and learning behavior. The analysis results show that the dropout behavior is related to factors including the source country, the active time, the video playing time, and the cumulative learning days. Specifically, the dropout rate of students whose source country is China is relatively high. This may be because learners from China are restricted by objective conditions such as language and network access, which affects their learning behavior on the EdX platform. Students with less active time, fewer videos playing time, and fewer cumulative learning days are more likely to drop out of MOOC, which indicates that students' online participation is more accurate and objective in measuring their learning behavior [30]. However, gender, age, academic qualification, and the number of forum posts have little impact on the dropout behavior. Earlier, [PERSON] also pointed out in his work [5] that the number of forum posts had little influence on dropout behavior. The MOOC dropout rate prediction method proposed in this paper uses three common machine learning methods to predict the dropout rate on the basis of statistical analysis and visualization of various factors in the data set. The experimental results show that the overall prediction results of the three models are significantly better than those of the models without statistical analysis steps. Although the prediction algorithm used in this study is relatively traditional, it has achieved good prediction results. This shows the importance of statistics and analysis of various factors that may cause dropout. In addition, the dropout behavior defined in this paper refers to the total score of 0 during the whole  
Figure 11: ROC curve of model prediction effect comparison. learning period. However, the dropout behavior defined in the previous literature refers to learners do not have any online learning activities for seven consecutive days. The author believes that MOOC learners are mostly part-time learners, that is, they usually do online learning while working (or completing their offline learning). It is normal that there is no learning activity for the whole week occasionally. This may be another reason why the results of this paper are better than other works. In addition to the above analyzed factors, there are many other factors that affect the dropout behavior of MOOC learners, such as teachers' teaching factors (teachers' accents, teaching contents, etc. ), which cannot be fully covered in this paper, but the unselected factors may have a great impact on the dropout rate. In future work, we will investigate how other factors from multiple sources can be combined to enhance the dropout prediction of MOOCs and explore other machine learning models to further improve the accuracy of dropout prediction. Finally, we hope that the definition and analysis method of dropout used in this paper can have some reference significance for the follow-up research.