Most of the existing knowledge tracing research use implicit information such as concepts contained in exercises or explicit information such as historical interaction data to model exercises, they don't pay attention to the heterogeneity of internal and external information, and lack heterogeneous fusion of internal and external information. To address these issues, this paper proposed a knowledge tracing model that integrated heterogeneous internal and external information. Firstly, this model calculated the relevance between historical concepts and current concepts based on implicit information like concepts, depicted the influence of historical concepts on the current ones and modeled the exercises' implicit representation. Secondly, it computed the relevance between historical exercises and current exercises by explicit information like interaction data, captured the impact of historical exercises on the current ones and established the exercises' explicit representation. Furthermore, this paper utilized the channel attention mechanism on the aforementioned internal and external exercises representation, and achieved a fusion of heterogeneous information to create the exercises' heterogeneous representation, enabling the prediction of learners' performance. To validate the performance and effectiveness of the proposed model, this paper conducted comparative experiments on three real-world datasets using four relevant baseline models. The experimental results demonstrate that the proposed model achieves superior performance on multiple evaluation metrics. Additionally, ablation experiments confirm the effectiveness of the proposed model in better modeling exercise representations by incorporating both internal and external information. In the terms of applications, it designed a smart learning environment to prove the advantages of the proposed model in actual teaching scenarios.

[2023-11-17] Accepted Paper /doc/2023.07.0308/v1

[2024-03-05] Printed Article /doc/2023.07.0308/v2