Discovering Multi-Relational Integration for Knowledge Tracing with Retentive Networks
Zhou Linhao, Sheng-hua Zhong, Zhijiao Xiao · 2024
Knowledge Tracing (KT) focuses on estimating students' knowledge states and predicting their future performances, which is a crucial task for online education platforms. In light of the advancements in educational big data and deep neural networks, numerous KT models have been proposed and promising outcomes have been achieved. Nevertheless, we have noted that current methods possess certain evident constraints. Thus, we propose a Knowledge Tracing model with Multi-Relational Integration (MRIKT): (1) we consider the more sophisticated relations between questions and skills, which can reveal deeper patterns of students' learning; (2) we emphasize the forgetfulness nature of students and the value of inter-exercises relations by incorporating a retentive module. Specifically, we choose graph convolutional networks to construct the advanced-relation between questions and skills, named graph representation module. Additionally, by linking different exercises, our novel retentive module, inspired by RetNet, can acquire valuable insights. We extensively evaluate the performance of MRIKT on three real-world datasets. The results demonstrate that MRIKT achieves outstanding performance, which improves at least 8.44% compared to baseline models.