Knowledge tracing (KT) is an intelligent educational technology used to model students’ learning progress and mastery in adaptive learning environments for personalized education. Despite utilizing deep learning models in KT, current approaches often oversimplify students’ exercise records into knowledge sequences, which fail to explore the rich information within individual questions. Additionally, existing KT models tend to neglect the complex, higher-order relationships between questions and latent concepts. Therefore, we introduce a novel model called Feature Crosses Information-based Knowledge Tracing (FCIKT) to explore the intricate interplay between questions, latent concepts, and question difficulties. FCIKT utilizes a fusion module to perform feature crosses operations on questions, integrating information from our constructed multi-relational heterogeneous graph using graph convolutional networks. We deployed a multi-head attention mechanism, which enriches the static embedding representations of questions and concepts with dynamic semantic information to simulate real-world scenarios of problem-solving. We also used gated recurrent units to dynamically capture and update the students’ knowledge state for final prediction. Extensive experiments demonstrated the validity and interpretability of our proposed model.

The knowledge tracing (KT) aims to track changes in students’ knowledge status and predict their future answers based on their historical answer records. Current research on KT modeling focuses on predicting student’ future performance based on existing, unupdated records of student learning interactions. However, these approaches ignore the distractors (such as slipping and guessing) in the answering process and overlook that static cognitive representations are temporary and limited. Most of them assume that there are no distractors in the answering process and that the record representations fully represent the students’ level of understanding and proficiency in knowledge. In this case, it may lead to many lack of synergy and incoordination issue in the original records. Therefore we propose a cognitive representation optimization for KT (CRO-KT) model, which utilizes a dynamic programming algorithm to optimize structure of cognitive representations. This ensures that the structure matches the students’ cognitive patterns in terms of the difficulty of the exercises. Furthermore, we use the co-optimization algorithm to optimize the cognitive representations of the subtarget exercises in terms of the overall situation of exercises responses by considering all the exercises with co-relationships as a single goal. Meanwhile, the CRO-KT model fuses the learned relational embeddings from the bipartite graph with the optimized record representations in a weighted manner, enhancing the expression of students’ cognition. Finally, experiments are conducted on three publicly available datasets respectively to validate the effectiveness of the proposed cognitive representation optimization model. The source code of CRDP-KT is available at https://github.com/bigdata-graph/CRO-KT.