## 2 Problem Definition and Related Work
### Related Work  
Online learning behavior analysis has become one of the most concerning issues in learning analysis and educational data mining. Researchers identify learning behavior patterns [8; 9; 10; 11], measure learning engagement [12; 13; 14], identify cognitive styles [15; 16], set up students' portraits [17; 18] and recommend learning resources [13; 19] by analyzing student records of online learning behavior. The results of these studies reflect that the records of students' online learning behavior contain a lot of important information in the learning process. [PERSON] et al. extracted 20 features from the logs of an LMS to describe learners' online learning behavior. These features were statistics of students accessing different online learning activities. They also used the Random Forest method to establish a prediction model of students' performance, expressed in six grades, A-F, with an average prediction accuracy of 49% in five types of courses [5]. [PERSON] et al. gathered the logs of students' watching videos in two courses from MOOC, extracted 16 features of watching videos and 8 features of answering questions, and established a regression model of course scores using a feedforward neural network. Under the condition that the model contained eight hidden layers, the Mean Absolute Error (MAE) between the prediction results and actual scores was reduced to 6.8 [6]. [PERSON] et al. collected logs regarding the mixed teaching of information technology courses in three universities from SPOC, but did not specify what features were created to describe online learning behavior. The experimental results showed that the prediction model established using the ECOC algorithm had a higher prediction accuracy than the classical SVM method, with a prediction accuracy of 80% [7]. [PERSON] et al. collected logs of 488 undergraduates in 10 courses from Moodle, which is the most famous LMS, and extracted 10 indicators of activity engagement, including counting-based and duration-based features. They used the Partial Least Squares (PLS) method to establish a structural model of online behavioral engagement and learning achievement and analyzed the online engagement activities that have the greatest impact on learning achievement [20]. They extracted eight counting-based behavioral features from the logs and established a four-class prediction model using the decision tree, achieving a prediction accuracy of 70%. Because the decision tree is a typical white box model, they also extracted the most important six rules that affected academic performance from the established prediction model [21]. [PERSON] et al. extracted 19 behavioral features in the five stages of preparation, progress, resource learning, forum interaction, and test from the logs, and established a prediction model using logistic regression, achieving a prediction accuracy of 95% [22]. [PERSON] et al. paid attention to the performance of online short-term courses, used the content features and behavioral features discussed in forums as indicators, and established prediction models using four classifiers: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Linear Discriminant Analysis (LDA), and Random Forest (RF). The experimental results showed that the performance of classifiers in different courses was not consistent, and the highest prediction accuracy was 89% [23]. [PERSON] et al. constructed a student-problem interactive network using the mouse logs of students in the interactive online question library and used a Graph Neural Network (GNN) to predict students' performance, achieving an accuracy of 66% and 55% in short-term and long-term predictions, respectively. The prediction accuracy of their network was higher than the three classic classifiers of Gradient Boosting Decision Tree (GBDT), SVM and Logistic Regression (LR) [24]. [PERSON] et al. extracted the learning behavioral features from the logs of an LMS and trained a bidirectional Long Short-Term Memory (LSTM) Recurrent Neural Network, which achieved an 80% prediction accuracy [25].