# ContextKT: A Context-Based Method for Knowledge Tracing
## 2 Related Works  
In this section, we introduce the works on knowledge tracing. Based on the techniques used in knowledge tracing methods, they can be divided into three categories: Bayesian Knowledge tracing (BKT) models, factor analysis-based models, and deep learning-based knowledge tracing models. In addition, we also introduce relevant studies which utilize context information in their works. **Bayesian knowledge tracing models** usually utilize historical interaction records of students to model their knowledge states based on Hidden Markov Model, which is first provided by [PERSON] and [PERSON] [1]. There are also many works on improving the BKT-based model. [PERSON] and [PERSON] [15] provide the Prior Per Student model, which can learn individualized parameters in a single step to accelerate the learning of global optimal fit parameters. To represent multiple skills within a single model, [PERSON] et al. [16] designed a knowledge tracing model based on Dynamic Bayesian networks so that they can model prerequisite hierarchy and relationships among different skills in a learning domain. [PERSON] and [PERSON] [6] focus on saving time and problems in programming tutors with the help of BKT. For each question provided by tutors, they calculate slip and guess parameters in modeling based on an empirical approach to evaluate students' mastery states. [PERSON] [5] improves BKT by adding the Elo component into the model, which conduces to infuse students' problem-solving skills with BKT. In these BKT-based models, the knowledge states of students are treated as vectors for the Hidden Markov Model, which cannot represent the mastery of each individual knowledge concept, so these models cannot efficiently describe the students' mastery level of knowledge. **Factor analysis-based models** take advantage of latent factors to predict the accuracy of students' records. For example, [PERSON] et al. [17] improves a cognitive model by combining statistical model, human expertise, and A* search to model students' records. To trace individual students with their individual skills, [PERSON] et al. [18] provided a performance factor analysis model. It is sensitive to the indicator of students learning performance by generating logit value so that this model can estimate the cost of each action in instructional engineering. [PERSON] et al. [19] utilized factorization machines in the existing model, including AFM [17] and PFA [18], to deal with sparse students' data. [PERSON] et al. [7] involves both multiple skill tags and memory decay in the knowledge tracing model to extend DASH [20], which is a factor analysis-based model combining collaborative filtering and the psychological theory of memory. [PERSON] et al. [13] provided AKT, which embeds contexts by monotonic attention mechanism and utilizes the Rasch model to capture the difference among students on the same question. Unlike our model, AKT is an IRT-based model and we designed a deep learning-based model. In addition, the AKT input the embedding vector into the Rasch model for prediction, while we combine outputs of LSTM and multi-head attention mechanism to enhance accuracy. Compared with BKT-based models, IRT-based models involve the characters of students and questions, so these kinds of models are more interpretable than BKT-based models. **Deep learning-based knowledge tracing models** utilize neural network techniques, such as Long and Short-Term Memory network (LSTM), Recurrent Neural Network (RNN), and Graph Neural Network (GNN) on the knowledge tracing tasks. They usually have a sequential structure, which can model the study process to retain the study trajectory information [21]. With the help of neural network techniques, deep learning-based models have a more powerful ability on cognitive processes. Deep Knowledge Tracing (DKT) [22] applied RNN to track students' knowledge states based on their learning history. Compared with BKT-based models, DKT can achieve high performance without human annotation. [PERSON] et al.