Abstract: Knowledge tracking technology,which models students' past response information to accurately predict their mastery of various knowledge concepts and future learning performance,has become the core and key of building intelligent educational systems.With the development of deep learning,research methods for knowledge tracking have become increasingly diverse.However,the relationship between questions andknowledge points is complex and implicit,making it difficult for models to accurately uncover their underlying relational features in the absence of expert annotations.To address the limitations of existing methods in modeling the relationship between questions and knowledge points and their poor interpretability,this paper proposes a memory-augmented knowledge tracing model based on relational learning.Firstly,the model employs a self-supervised relational learning module composed of multi-layer Transformers to effectively model the relational features between questions and know-ledge points.It also incorporates dynamic multi-head attention to extract key information from question sequences,enhancing the model's ability to handle long-term dependencies in sequences.Then,a dual-matrix knowledge memory storage module is used to dynamically model students' mastery state of each knowledge point and predict their learning performance.Finally,a PGD-based adversarial training method is applied to generate adversarial samples for joint training,improving the model's generalization abi-lity.Comparative experiments with seven representative models on three knowledge tracking datasets demonstrate that MKTRL achieves improvements in both AUC and ACC metrics.Multi-dimensional experiments further validate the predictive effectiveness of the proposed mode.

Key words: Knowledge tracking, Relational learning, Adversarial training, Memory-enhancing neural networks, Attention mechanism, Intelligent education