## 2 Related Work  
Student performance prediction research can be categorized into regression and classification problems, where the former predicts a score value, and the latter predicts a category [11][PERSON] Earlier studies in student performance prediction focused on traditional machine learning methods, which used logistic regression [12], support vector machines [13], and decision trees [14] to establish models for predicting student performance. In a study [15], an automatic method for observing and predicting student grades was proposed. This method utilized a genetic algorithm to capture the 30 best attributes from students' historical learning data and trained a K-NN regression model and a decision tree using these features and labels to predict students' performance score and categories. [PERSON] et al. [16] used naive Bayes, random forest, decision tree, AdaBoost classifier, logistic regression, and KNN algorithms as basic learners, support vector machine (SVM) as meta-learner, and created a stacking method to develop a hybrid ensemble model. By analyzing the data, they discovered important features that influence student learning outcomes and successfully helped teachers identify students at risk. [PERSON] et al. [17] and [PERSON] et al. [18] combined the SMOTE technique with machine learning techniques to improve the impact of data imbalance on the model and enhance the accuracy of student performance prediction. [PERSON] et al. [19] proposed a method for predicting student performance based on time-series clustering. The method [PERSON] learning behavior data such as the frequency of accessing course materials, frequency of reading forums, number of discussions, and number of replies posted to identify at-risk students and predicts student performance more accurately than traditional frequency aggregation methods. Traditional machine learning methods ignore time information in the original data, which cannot effectively capture the impact of time features on student performance. However, deep neural networks can effectively address the problem of time information missing [20]. Therefore, some researchers have begun to utilize deep learning techniques to predict student performance. For example, [PERSON] et al. [21] used a GRU network to extract time-series features from clickstream data and assessment score data and combined them with demographic features to predict student performance. [PERSON] et al. [22] proposed a hybrid deep learning model that can extract time-behavioral and overall behavioral information from learning behavior data to more accurately predict high-risk students. [PERSON] et al. [23] constructed a student performance prediction framework with an attention mechanism, in which an LSTM neural network was used to reflect students' learning processes, and a DSP-based adapter was used to enhance the importance of key information and improve the accuracy of student performance prediction. [PERSON] et al. [24] proposed a transformer-based method for predicting student performance by converting the learning behavior data of students into a sequential feature vector. In some studies [25; 26; 27], authors used convolutional neural networks (CNN) to extract high-dimensional time information from the time series of student activities to better extract spatio-temporal features and utilized LSTM to capture the sequence information of student learning dynamics to more accurately predict student performance. [PERSON] et al. [28] proposed a performance prediction model based on the differences in patterns of student behavioral features for students with significant performance changes, which uses a multi-head attention mechanism to automatically select more important higher-order behavioral combinations of features, maintain higher temporal accuracy, and can predict student performance more accurately. [PERSON] et al.