# ContextKT: A Context-Based Method for Knowledge Tracing
## 1 Introduction
We can collect these questions with knowledge concepts and students' corresponding responses as an interaction log to model the student's knowledge state. Besides this interaction log, students' mastery of knowledge is also affected by the Ebbinghaus Forgetting Curve [11]. Therefore, we can consider both interactions and the forgetting behavior to model students' performance and predict whether they can give a response to the next question correctly. Students' performances on a new question are based on their previous performances on the questions. To comprehensively capture the studying behaviors, we should combine the contexts of students' responses to historical questions with their knowledge masteries  
Figure 1: Overview of Context-based Knowledge Tracing. and the knowledge concepts contained in each question. Recently, some models [12; 13; 14] have been designed for knowledge tracing using this information. Some of them analyze the interactions between students and questions for prediction. However, these models only consider practices and knowledge concepts on knowledge tracing, but ignore other factors that affect students' performances. In fact, there are multiple factors affecting the performance of the student, such as the student's memory and behaviors. In addition, their influence is a long-term effect on the study process. Therefore, knowledge tracing cannot be only modeled by the practices with corresponding knowledge concepts. The challenges are how to deal with the influence changes of these factors temporally and how to deal with the interaction influence of these factors. To involve multiple factors to predict students' performances accurately, we design the ContextKT model with more context factors on knowledge tracing. To address these challenges, we utilized the LSTM model to capture the context information of memory and forgetfulness, and the multi-head attention mechanism to capture the context information of students' behaviors. In addition, we also consider the personalized information in the questions for prediction, including the contained knowledge concepts and the similarity among questions. In summary, the contributions of our work are given as follows:  
1. We propose a context-based approach for knowledge tracing. On the factors that affect students' performance, we define five contexts to build the prediction model and design the data structure and the models to describe or capture the context information. 2. A QR-matrix is defined to represent the relationship among questions, knowledge concepts, and responses, which is used to describe the context information about questions and knowledge. An improved LSTM-based model is proposed to capture the context of students' memory and forgetfulness. A multi-head attention mechanism is designed to capture the context information of students' response behavior. 3. A context-based prediction model ContextKT is established on top of the improved LSTM model and the multi-head attention mechanism. For optimization, the regularization item is designed to consider the question similarity and the graph Laplacian matrix is used to reduce the computation complexity. 4. Our prediction model is evaluated on two real educational datasets. The experimental results show our model can achieve high performance on both datasets, and outperform the baseline methods. The rest of the paper is organized as follows. We review the related works in Section 2. The problem of knowledge tracing is formulated in Section 3. Then we present our ContextKT in Section 4 and evaluate it in Section 5. Finally, we give the conclusion in Section 6.