# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 1 Introduction  
With the rapid developments in artificial intelligence technology and online education systems, knowledge tracing (KT) [1], as the core technology of educational artificial intelligence, has attracted increasing attention from researchers. Through the assessment of students' knowledge states derived from their learning interactions, KT can improve learning efficiency and facilitate a better understanding of the learning process for students. Nevertheless, most existing KT methods [2, 3, 4] assess the students knowledge state without considering the high-order relations between the exercises and knowledge concepts. In actuality, the relations between the exercises and concepts have a profound impact on learning progression and significantly influence future performance predictions. Research on knowledge tracing (KT) began in the 1990s, with [PERSON] and [PERSON]'s [5] foundational work, which is widely regarded as the first major contribution to the field and the origin of the KT concept. Advances in deep learning subsequently spurred the rapid development of numerous KT models. For instance, deep knowledge tracing [2] pioneered the use of deep learning for KT, while memory-augmented KT models were introduced by [PERSON] et al. (2017) [3]. More innovations include attention enhanced KT frameworks proposed in several studies [6; 7; 8] as well as graph neural network (GNN)-based approaches [9; 10; 11] that model relational structures, such as knowledge component (KC) similarities, dependencies, and question-KC mappings, to enhance performance. Beyond diverse neural architectures, KT research progresses using two main types of models. The first models leverage educational data features, including exercise content [12], difficulty levels [13], relationships between exercises and concepts [14], and cold start [15]. The second models incorporate psychological studies on student learning and forgetting behavior [16; 17; 18; 19], considering additional factors that influence knowledge state tracing, such as the time interval between a student's interactions and the number of times that the knowledge concepts have been practiced. The rise of large language models (LLMs) has spurred the emergence of knowledge tracing frameworks [20; 21] that integrate LLM capabilities with sequential interaction modeling. This integration addresses the intricate exercise-knowledge component relationships that are prevalent in real-world educational scenarios. While GNN-based methods like GKT [10] capture skill dependencies through pairwise graphs, they fail to model high-order exercise-concept correlations. In contrast, HGKT's hypergraph convolution explicitly resolves multi-hop correlations, overcoming limitations in graph-based KT. Similarly, LLM-enhanced frameworks (e.g., LLM-KT [21]) treat exercise IDs as mere numerical tokens, failing to interpret sequential interactions that reflect knowledge states. This fragmented tokenization obscures behavioral semantics. HGKT achieves higher interpretability in knowledge state evolution. Existing KT approaches [3; 4] typically construct predictive models based on knowledge concepts rather than target questions. In KT, it is common to encounter scenarios with several concepts and numerous exercises, where one concept may be associated with many exercises, and a single exercise may correspond to multiple concepts. However, these knowledge tracing (KT) approaches treat knowledge concepts as independent entities, neglecting their inherent hierarchical and composite relationships, which can result in degraded performance. For example, in Figure 1, there are four exercises that are associated with more than one concept. Despite the fact that exercises e2 and e3 assess identical concepts, different difficulty levels may yield different correct response probabilities. Therefore, it is important to research the high-order correlations between the exercises and concepts. Figure 1: A toy example of exercises—concept relation graph.