Modeling Learning Transfer Effects in Knowledge Tracing: A Dynamic and Bidirectional Perspective

Weizhe Huang, Shuanghong Shen, Zhenya Huang, Qi Liu, Junyu Lu, Yu Su

Abstract: Knowledge Tracing (KT) is a crucial task in online intelligent education systems, which aims to dynamically monitor students’ evolving knowledge state. During students’ learning process, not only do knowledge states on later-learned concepts impact the understanding of earlier-learned concepts, but the knowledge states on earlier-learned concepts also influence the learning of later-learned concepts, forming bidirectional learning transfer. However, existing work typically focuses on either the backward impact of the currently learned concept on previously learned concepts or the forward impact of previously learned concepts on future concepts. Moreover, they commonly assume the transfer influence weights between concepts remain fixed throughout the entire learning process, thereby neglecting students’ dynamic transfer mechanisms. In this paper, we introduce the Dynamic Bidirectional Transfer Knowledge Tracing (DBTKT) model, which simultaneously takes into account learning transfer effects in both directions and utilizes students’ personalized learning experiences to measure dynamic transfer influence weights. Extensive experimental results on public datasets demonstrate that our model not only generates improved performance predictions compared to existing methods but also offers meaningful insights into knowledge state evolution from a learning transfer perspective.

External IDs:dblp:conf/dasfaa/HuangSHLLS24