Heterogeneous graph-based knowledge tracing with spatiotemporal evolution

Abstract

Knowledge tracing (KT), in which the future performance of students is estimated by tracing their knowledge states based on their responses to exercises, is widely applied in the field of intelligent education. However, existing mainstream KT models explore the importance of knowledge relations but ignore the key role of cognitive factors. According to the knowledge construction theory, the human cognitive system performs both spatial accommodation and temporal assimilation to internalize knowledge. In this paper, we propose an innovative heterogeneous graph-based Knowledge tracing method with spatiotemporal evolution (TSKT), in which knowledge state evolution is traced along both temporal and spatial dimensions. We construct a heterogeneous graph with multiple exercise attributes, including content, concepts, and difficulty, to obtain a knowledge space with richer exercise representations through hierarchical aggregation. We design a spatial updating module in which each interaction updates the current node’s state of the knowledge space and transfers its influence to its neighbors. We also design a temporal updating module to further update the knowledge state through short-term memory enhancing and long-term memory forgetting. Finally, we stack these modules to obtain deeper features by using alternate spatiotemporal updating. Extensive experiments on three datasets reveal the superiority of the proposed method and its variants in terms of future performance prediction.