## 2 Related Work and Motivation  
The work of KT is partitioned into two phases in general: the development of KT models on the basis of traditional research methods and the development of KT models on the basis of deep learning. The first stage of KT development is from 1994 to around 2015. During this period, the research on KT was mainly based on traditional research methods, such as probability maps and psychological theories. Among them, probability map-based models represented by Bayesian knowledge tracing (BKT) [8] were the main part. BKT took the knowledge state of whether students mastered a knowledge point or not as a hidden variable, the result of whether they answered correctly the corresponding questions of the knowledge point or not as an observation variable, and used the hidden Markov model to represent the changes of the skills status of learners, and then predicted the probability that learners acquired the skill point. BKT solved the problem of KT for the first time. Subsequent research has also produced some variants of BKT that improved the performance of BKT in different ways. For example, [PERSON] et al. proposed introducing a layer structure to represent the prerequisite relationships in actual educational scenarios, consequently acquiring a more accurate model used for predicting students' knowledge status [18]. KT-IDEM [10] was proposed to more accurately predict students' practice performance by adding the dependence of item difficulty to the probability map. BKT and its variants had simple structures and strong explanatory ability. During this period, BKT was of great significance in constructing intelligent tutorial systems and learning management systems, such as the ACT Programming Tutor [8] and edX [3]. For example, BKT improved the prediction of the test performance on the ACT Programming Tutor of students through modeling the change of the probability that the student grasped each rule [8]. EdX used variants of BKT to acquire students' practice performance on several questions in the past period, modeling students' mastery of knowledge components and, thus, predicting students' test performance in a later period. This helps to analyze the learning effect of students on the edX platform [3]. However, BKT and its variants ignored the long-term temporal dependence of students learning process because they were based on a Markov chain structure. Furthermore, BKT was not suitable for future scenarios with large amounts of data, because it relied on labeling the data manually. In addition, some scholars have applied psychological theory to KT, such as item response theory (IRT) [19], DINA [20], and so on. Although models based on psychometric measurement theory could be well interpreted, the simple parameter settings limited the models' ability to encode complex features. Therefore, though a number of effective models emerged in the first stage of KT, the model structures were too simple to express more complex features. Moreover, there was a bottleneck in the development of data processing due to the need for manual operation. The second stage of KT is from 2015 to the present. In 2015, deep knowledge tracing (DKT) [12] was proposed, marking the beginning of an era in which DL technologies drove the evolution of KT. DKT used long short-term memory (LSTM) to learn the procedure that the learners' skill status changed and made a prediction about learners' future exercise performance. Compared with BKT, the LSTM model used by DKT could make use of long-term time series dependence, which confirmed to the long-term dependence of the state of knowledge at each moment in the actual learning process and helped to obtain more accurate prediction results. In addition, DKT could automatically extract features without labeling them, which saved labor costs and avoided human errors. However, DKT only considered whether students answered correctly or not, ignoring other characteristics of the learning process. [PERSON] et al.