Attention-Based Difficulty Feature Enhancement for Knowledge Tracing

The task of knowledge tracing aims to monitor students’ knowledge states through their historical answer records and predict their future performance in answering questions. In recent years, knowledge tracing models based on deep learning have exhibited significantly higher accuracy in prediction compared to traditional knowledge tracing models. However, existing models often fail to fully leverage the impact of difficulty factors on students’ knowledge states. In this paper, to better exploit the difficulty factors for enhancing the discriminative power among different questions, we propose a novel Difficulty-Fusion Knowledge Tracing(DFAKT) model. Specifically, we design a DEI(Difficult-Enhance Interaction) method for extracting difficulty features by integrating skill difficulty and question-specific difficulty information. Subsequently, we develop a Difficulty-Fusion module designed to integrate the student interaction information, which includes features such as answers and knowledge points, with the difficulty feature. Finally, recognizing that different exercises have varying impacts on students’ knowledge states, we incorporate an attention-based GRU module in the model to dynamically aggregate the knowledge states from previous time steps. This allows the model to focus more on questions with a greater impact on knowledge states, thereby improving prediction accuracy. Experimental results demonstrate that the proposed model outperforms existing models in predictive performance on large-scale real-world datasets, and the effectiveness of each module is validated.