## 2 Related Works  
At present, knowledge-tracking models can be divided into two types: knowledge-tracking models based on probability graphs and knowledge-tracking models based on neural networks. These are respectively introduced in the following.  
Regarding knowledge-tracking models based on probability graphs, [PERSON] proposed Bayesian knowledge-tracking using a machine-learning algorithm [6], which had good interpretability but required manual skill annotation by human experts and was costly.  
With regard to knowledge-tracking models based on neural networks, in recent years with the development of deep neural network technology, [PERSON] input students' interactive records into LSTM [7] or RNN models and used hidden states of neural networks to represent students' knowledge states, proposing deep knowledge tracking (DKT) [8]. Compared with the previous Bayesian knowledge-tracking model, the improvement in AUC evaluation metrics was close to 20%, which also has allowed a wide range of scholars to see the huge development potential of deep knowledge tracking. [PERSON] et al. [9] improved DKT by adding regularization terms into the loss function, which alleviated the problem of large fluctuation in student state prediction in DKT. [PERSON] proposed the DKVMN (dynamic key-value memory network) [10] model in 2016, which used a static matrix to store skills and a dynamic matrix to update students' mastery of the skills after answering each question. The DKVMN model solved the drawback of the DKT model, which could not extract students' mastery of each skill well. [PERSON] in [11] used a hierarchical memory network to simulate long-term and short-term memory and restore the human memory mechanism in a knowledge tracking task. [PERSON] in [12] proposed a relationship-aware self-attentive mechanism by which the forgetting behavior of students was simulated. [PERSON] and several other scholars in [13; 14; 15] applied graph neural networks to the knowledge tracking domain to enhance the prediction by using the powerful expressive power of graph neural networks. In general, deep-learning-based knowledge tracing and its improved models show significant performance improvements compared to the Bayesian knowledge-tracking model but suffer from the problem of not being able to fully learn effective features when interaction records are too sparse due to limitations of their structure.  
To address the shortcomings of deep-knowledge-tracking models, many scholars have made improvements:  
[PERSON] considered the characteristics of students' behavior and learning ability in DKVMN-LA [16], dynamically simulated student ability, and improved the accuracy of prediction, but failed to conduct an in-depth exploration of question information. The EKT [17] model [PERSON] proposed was based on the DKT model and used the text information of the question to enhance the representation of the question, alleviating the model underfitting problem caused by sparse interactive records, but the model lacked research on student ability. In addition, there have also been attempts to incorporate an attention mechanism [18] into knowledge-tracking models to improve the performances of the models, such as SAKT and [19] SAINT [20] using a Transformer [21] model based on a self-attention mechanism to assign weights to previous responses to extract key information, as well as [PERSON] et al. in [22], who improved the attention calculation of a Transformer model by considering the time interval between answers, thus simulating the forgetting behavior of students and optimizing student modeling.  
Overall, all of the above models have made good progress in the area of knowledge tracking, but there is much room for improvement in prediction performance due to a lack of adequate consideration of question difficulty, as well as student ability.