## 1 Introduction  
At present, learning management systems (LMSs), are widely welcomed [1]. LMSs are software systems for distance education based on the internet. LMSs have the functions of managing students and learning resources, providing students with the services of online learning, exercises, tests, communication, registration, scheduling, logging, and so on [2]. The typical LMSs can be divided into categories of open LMSs (e.g., EdX, Moodle [3]), customized LMSs (e.g., ASSISTments [4]), and learning management ecosystems [2, 5], LMSs have rich resources, flexibility, and convenience, which brings new development opportunities for intelligent education [6]. As a crucial research branch of intelligent education, knowledge tracing (KT), which promotes solving the problem of personalized tutoring for learners, has attracted more and more attention [7]. KT can model and analyze the data of interactions from learners practicing online to obtain the potential law of the change of different students' knowledge status, and then predict students' future learning performance [8]. Specifically, knowledge tracing can model the students' practice process by logistic function, machine learning (such as hidden Markov models) or deep learning (such as recurrent neural networks, graph neural networks) algorithm models based on the students' practice records collected by LMSs such as ASSISTments and Coursera. Through the modeling and analysis of the interaction process between students and exercises, KT models can learn parameters about the hidden state of students' mastery of knowledge points (the probability of successfully mastering the corresponding knowledge points of exercises) from the explicit answer history sequence and then can output the prediction of whether the student can answer the next question correctly or not. This is because it is generally believed in the field of education that exercises can test students' grasp of the knowledge points contained in the exercises, and students' grasp of the knowledge points can also be reflected through exercises. KT can analyze and predict the learning status of different students, which is helpful for LMSs to provide personalized learning programs and help students improve learning efficiency [9]. When most students can achieve better learning results, the educational level and social economy are expected to usher in further prosperity and development. As a result, more research has been conducted on KT. Today, there are many KT models. First of all, Bayesian knowledge tracing (BKT) was proposed in 1994 [8], which took whether students have mastered a certain knowledge point or not as a hidden variable and used a hidden Markov model (HMM) to model its changes in the learning process. BKT has laid an important foundation for the development of KT. For a long period of time, the research on KT has mainly focused on the improvement and application of BKT, such as Knowledge Tracing: Item Difficulty Effect Model (KT-IDEM) [10] and Personalized Clustered BKT (PC-BKT) [11]. However, the traditional KT models represented by BKT were limited by manual tags and were limited by modeling and analyzing single concepts. Then, deep knowledge tracing (DKT) [12] was put forward in 2015, which applied recurrent neural networks (RNNs) to modeling the learning process of students and predicting their future performance. DKT solved the problem of the discrete modeling of knowledge points without manually annotating the relationship between exercises and knowledge points. Since then, more and more deep learning (DL) techniques have been introduced into KT modeling tasks. For example, dynamic key-value memory networks (DKVMNs) [13] brought in and improved memory-augmented neural networks (MANNs) to automatically discover knowledge points in exercises and predict students' knowledge state; convolutional neural networks (CNNs) were introduced into CKT [14] to analyze students' personalized phased learning characteristics and predict learning performance.