Knowledge tracing aims to model the state of knowledge and ultimately predict the future performance of learners by describing exercises through the representation of concepts. However, in terms of the representation of concepts, the current research doesn't model the influence of historical knowledge concepts on the temporal relationship of the current concepts, nor does it describe the role of the spatial relationship between various concepts in the exercise. In order to solve these problems, this paper proposed a knowledge tracing model characterized by temporal and spatial correlation fusion. First of all, based on the degree of temporal correlation between concepts, it modelled the temporal effect of historical concepts from current concepts. Secondly, it modelled the spatial interaction between several concepts contained in the exercise to obtain the representation of knowledge points containing temporal and spatial information through the graph attention network. Finally, it used the above representation of concepts to derive the representation of the exercises, and generated the current state of knowledge through the self-attention mechanism. In the experimental stage, this paper compared the performance of the proposed model with the five relevant knowledge tracing models on four real datasets. The results show that the proposed model has better performance. In particular, compared to the five comparative models on the ASSISTments2017 dataset, the AUC and ACC are improved by 1.7%~7.7% and 7.3%~12.1%, respectively. At the same time, the ablation experiment proves the effectiveness of modeling the temporal and spatial correlation between concepts, and the training process experiment shows that the proposed model has certain advantages in the representation of concepts and the modeling of their interaction relationships. The application examples can also show that the model has better practical results than other knowledge tracing models.

[2023-11-29] Accepted Paper /doc/2023.09.0414/v1

[2024-05-05] Printed Article /doc/2023.09.0414/v2