## 1 Introduction  
In recent years, massive open online courses (MOOCS) have received widespread attention. Because of their numerous resources and the excellent resources from many famous universities and the appearance of platforms such as edX, Coursera and Udacity, MOOCS have become more and more popular all over the world. However, due to the lower binding force of MOOC compared with the traditional classroom, many students have abandoned their learning courses due to some internal or external factors, resulting in a waste of educational resources. In order to reduce the occurrence of this phenomenon, researchers have focused on the research of MOOC learners' dropout behavior prediction, expecting to accurately find out the learners with dropout risk, and take intervention measures in advance to enable them to persist in learning, so as to improve the completion rate of the course [1]. Therefore, predicting MOOC learners' dropout tendency based on learning behavior has become a hot topic in MOOC big data analytics [2] and educational data mining research [3] field nowadays. MOOCs dropout prediction aims to find out the possibility of quitting the course at a certain time in the future based on learners' current learning behavior records [4]. Many scholars have been studying the problem of MOOCs learners dropping out of school. Some researchers used traditional classification methods such as logical regression LR [5, 6, 7],KNN [6], SVM [7; 8; 9] to establish prediction models. [PERSON] et al. extracted 19 features from three perspectives of click stream, homework test and forum behavior on the basis of courses data, regarded the whole learning cycle of learners as a time series, built a sliding window model combined with machine learning algorithm to dynamically predict the dropout rate of learners [8]. [PERSON] et al. extracted 19 student behavior characteristics which could be represented as single real numbers from click stream data, comprehensively ran logical regression and linear SVM methods to predict students' dropout behavior in the coming weeks, and concluded that adding forum data to the prediction in the previous weeks can effectively improve the prediction accuracy [7]. [PERSON] et al. used three classification features, namely, enrollment feature (Features characterizing learner's behavior in particular course), user feature (Features characterizing learner's behavior in platform) and course feature (Features characterizing course profile), to build Gradient Boosting Decision Tree model to predict the probability of students dropping out of school in the next 10 days [9]. Other researchers used neural network methods such as CNN [3; 10; 11] and LSTM [11; 12] to predict dropout behavior. Literature [10] designed a simple feature matrix based on time series, combining time information with students' behavior characteristics, and using convolutional neural network model to predict dropout. The proposed CNN model considers the local correlation of learning behaviors and improves the dropout prediction accuracy. Literature [12] regarded dropout as a time series prediction problem, constructed a time series of student behavior based on student click streams and forum data, and used LSTM to predict whether students would drop out of school. LSTM network outperforms other models in terms of AUC score. Literature [11] comprehensively considered the advantages of CNN and LSTM, proposed a hybrid network structure CLMS Net composed of CNN, LSTM and SVM. The proposed model can automatically extract features from student behavior data, and enhance the performance of dropout prediction.Although the above methods can obtain a good prediction effect, they have not analyzed the factors that affect MOOC learners' dropout behavior from a statistical perspective. [PERSON] et al.