关键词: 知识追踪, 学习行为, 变量选择网络, 选择性加权, 门控残差网络

Abstract: Existing knowledge tracing (KT) models often fail to effectively leverage learning behavior information and tend to overlook the varying contributions of different learning behaviors to students' performance. To address these limitations, this paper proposes a Learning behavior boosted knowledge tracing (LBBKT). The model employs a Gated Residual Network (GRN) to encode student learning behavior features into four contextual vectors, which are then integrated into the model. This approach fully utilizes learning behavior data, such as response speed, number of attempts, and hints, to better model the learning process. Additionally, the model applies a variable selection network to selectively weight student learning behavior features, while the GRN further suppresses irrelevant features, thereby amplifying the impact of relevant features on students' performance. Experimental results on multiple public datasets demonstrate that the LBBKT model significantly outperforms existing KT models in terms of prediction accuracy.

Key words: knowledge tracing, learning behavior, variable selection network, selective weighting, gated residual network