Knowledge Tracing Model Integrating Question Text Content and Cross-Level Contrastive Learning

Knowledge Tracing (KT) has garnered significant attention in the field of education, aiming to analyze learners' historical interaction records to dynamically track their current knowledge states and predict their future learning performance. Most existing KT models primarily rely on question IDs or concept IDs as the main features, neglecting question text content as a potential key feature. This limits the models' ability to learn semantic information from questions, ultimately affecting their predictive performance and accuracy. To address this issue, this paper proposes a novel model: the Knowledge Tracing Model Integrating Question Text Content and Cross-Level Contrastive Learning (QCKT). First, the model employs the BERT method to encode question text content, capturing its contextual semantic information and dependency relationships. Next, a textual attention mechanism module is introduced, enabling the model to dynamically adjust the importance of individual words to better focus on critical information in the questions. Finally, a cross-level contrastive learning module is designed to unify multi-level embedding features of question IDs and question text content within a shared embedding space, thereby optimizing the relationships between these features and enhancing the model's representation capability. Additionally, various data augmentation strategies are employed to increase data diversity, further improving the model's generalization and robustness. To evaluate the performance of the QCKT model, experiments were conducted on two publicly available datasets. The results demonstrate that, compared with existing KT models, QCKT achieves significant improvements in AUC scores, with increases of 2.21% and 0.85% on the respective datasets. This highlights its superior predictive capability, which indicates its considerable potential for accurately assessing students' learning states and providing personalized instructional recommendations.