Memory-Driven Attentive Knowledge Tracing for Implicit Inter-Question Analysis

Knowledge tracing (KT) plays a crucial role in personalized e-learning systems, aiming to model the current knowledge state by tracing the learner’s historical learning records to predict the learner’s future performance. However, most existing deep KT models do not take into account the correlation of questions and knowledge concepts, leading to inaccurate representations of students’ knowledge states. Additionally, obtaining relationships between concepts through manual tagging is not only prohibitively expensive, but also create subjective bias in the knowledge states representation. To address these challenges and explore implicit connections between questions and concepts, we propose a memory-driven attentive knowledge tracing (MAKT) model. The memory module in MAKT mitigates the subjective impact of manually labeling concepts by capturing information from learning interactions, fuzzily modeling the relationships between questions and concepts, and dynamically updating them. To further account for the complexity of learning behavior, we effectively decouple the impact of the question relevance and the learner’s forgetting effects using a linear bias attention mechanism, which enhances the interpretability of the model. We conducted extensive experiments on five publicly available datasets and showed that MAKT significantly outperforms previous knowledge tracing methods in predicting student performance.