# Hypergraph-Driven High-Order Knowledge Tracing with a Dual-Gated Dynamic Mechanism
## 5 Experiments
### Baseline Methods  
In order to evaluate the performance of our proposed model, we use the following models as our baselines:  
DKT [2]: DKT pioneered the use of deep neural networks for knowledge tracing. Utilizing recurrent neural networks (RNNs), it effectively models the complex temporal dynamics of student interactions and learning progressions. This enables more accurate predictions of student performance and mastery, significantly advancing educational analytics and personalized learning.  
DKT+ [25]: DKT+ represents an architectural refinement of the original deep knowledge tracing (DKT) framework, specifically designed to mitigate two critical limitations: input reconstruction deficiency, where the base model exhibits inadequate auto-regressive fidelity in reproducing observed interaction sequences and temporal prediction instability, characterized by inconsistent mastery trajectory projections for knowledge components across successive time steps.  
DKVMN [3]: leveraging memory-augmented neural architectures, the dynamic key-value memory network (DKVMN) model advances knowledge tracing capabilities. Its core framework deploys a static key matrix for persistent concept embedding and a dynamic value matrix capturing temporal mastery evolution. This dual-memory mechanism substantially enhances neural inference fidelity for latent knowledge state estimation. The innovative integration of associative memory modules yields superior accuracy and computational efficiency in learning progression modeling, ultimately enabling adaptive instructional optimization and measurable pedagogical outcomes enhancement.  
AKT [6]: AKT is a context-sensitive knowledge tracing framework that incorporates dual self-attention mechanisms to separately model exercise characteristics and learner responses. Its core innovation lies in a knowledge retriever module that leverages attention mechanisms to dynamically retrieve historical knowledge states pertinent to the target exercise, enabling contextualized prediction of learner proficiency.  
SAKT [26]: SAKT model predicts student mastery by identifying the most relevant past knowledge components (KCs) for a given target KC. It leverages attention mechanisms to focus on a select subset of a student's previous activities for each prediction. This targeted approach, relying on fewer KCs than RNN-based models, significantly mitigates the data sparsity problem common in knowledge tracing.  
LPKT [19]: The learning process-consistent knowledge tracing (LPKT) framework operationalizes knowledge state monitoring by explicitly simulating cognitive learning dynamics. This approach synergistically incorporates temporal features--response latency and inter-practice intervals--to quantify incremental learning gains and memory decay across successive learning events. Mnemonic reinforcement is achieved through empirically validated forgetting curve integration, ensuring longitudinal prediction stability. Foundationally, LPKT employs a static Q-matrix to establish binary exercise-concept mappings, preserving essential assessment-skill relational semantics.