# Knowledge Tracing Model and Student Profile Based on Clustering-Neural-Network
## 1 Introduction  
In the age of intelligence, online learning techniques have rapidly developed, facilitated by massive educational data. This data-driven approach enables the provision of intelligent education services. By utilizing vast educational datasets, it is possible for teachers to extract and analyze students' knowledge status, which provides them with valuable insights into students' needs. In this case, teachers can offer personalized instruction that is tailored to each student's individual requirements [1]. Knowledge Tracing (KT) is a technology that captures the state of students' knowledge by learning their behavior when answering questions. It is widely studied by worldwide researchers to enhance personalized learning. With massive learning data, KT models tend to become more complex. Recently, the state-of-the-arts (SOATs) are based on deep learning techniques and show significant improvements compared with Bayes Knowledge Tracing (BKT). Previously, KT models based on Deep Neural Networks (DNN) have been widely studied and improved. Some works improve the prediction accuracy of Deep Knowledge Tracing (DKT) through data engineering [2, 3, 4, 5, 6]. For example, [PERSON] proposes question-level Deep Knowledge Tracing (qDKT), which provides richer information and more accurate prediction by estimating students' master's of each question [4]. [PERSON] introduces a Classification and Regression Tree (CART) to filter all the features in the dataset to improve the characteristics of the input layer [6]. Furthermore, KT models based on the attention mechanism have beenwidely focused upon in recent years, and they can obviously improve the prediction performance of DKT [7; 8; 9; 10; 11]. In these works, some approaches improve the model details combined with educational and cognitive theory. For example, [PERSON] considers adding the \"mistake\" and \"guess\" factors to the KT model to improve the prediction accuracy of the model [12]. [PERSON] believes that the relationship of knowledge points has an impact on sequence prediction, so they used an attention mechanism to describe the relationship between knowledge points [13]. As for DNN-based KT models, interpretability is also an important aspect because students and teachers need clear information to guide them to improve deficiencies. Thus, some works focus on improving the interpretability of KT models [14; 15]. For example, [PERSON] uses a KT model to predict problem difficulty and student ability, improving the interpretability of the model. Using Item Response Theory (IRT) to predict student reactions based on problem difficulty and student abilities [14], [PERSON] expands each student's knowledge state vector into a knowledge state matrix that updates over time, capturing the student's knowledge state and improving the interpretability of the model [15]. Current works rarely consider improving the performance of KT by classifying the students and questions. We consider that different students have different reactions to different kinds of questions. Thus, if we use all students' data and questions to train one KT model, the accuracy of models would be impacted by the noises between different groups of students and questions. In this work, we conduct an experiment (in Section 1.1) to find that clustering data with different characteristics can improve the prediction accuracy of the overall model. In this case, the most related work is the work of [PERSON]. They believe that different methods for predicting students' learning abilities are different, so they cluster students' abilities based on the K-means clustering algorithm, using students' abilities as additional inputs to improve their prediction ability [16]. However, they only considered the characteristics of the students, not the characteristics of the questions. In this work, we classify both students and questions to improve the accuracy of models.