As an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing Knowledge Tracing (KT) tasks. However, existing KT methods only consider the positive learning transfer and disregard the negative learning transfer. Thus, this paper proposes an innovative positive and negative Learning Transfer-based Knowledge Tracing model (LTKT), which makes the first attempt to concurrently utilize the positive and negative learning transfer relations among concepts to improve KT results. First, LTKT constructs a learning transfer graph. Then, a direct learning effect component and a learning transfer effect component are carefully designed in LTKT. The first component quantifies the impact of an exercise’s practice result on the concept examined in the exercise. The second component, in contrast, computes the impact of the result on the concept’s neighbouring concepts in the constructed learning transfer graph, considering the positive and negative learning transfer phenomena. Extensive experiments on publicity datasets demonstrate that LTKT outperforms all state-of-the-art KT methods.

Deep learning has found widespread application across diverse domains owing to its exceptional performance. Nevertheless, the lack of transparency in deep learning models’ decision-making processes undermines their usability, especially in critical contexts. While researchers have made noteworthy advancements in explaining these models, they have frequently overlooked the differences between static and temporal models during explanation generation. In temporal models, features change over time, posing new challenges in the generation of explanations. Though extensive research has been dedicated to surmounting these hurdles, a survey summarizing these contributions is currently absent. To bridge this gap, this paper endeavors to summarize existing methods and their contributions in terms of both static and temporal models, highlighting their disparities. Additionally, we propose an innovative classification approach based on the comprehensibility of explanations, demonstrating that different explanation methods vary in their understandability for users. Finally, to assess the limitations of the explanation capabilities of existing methods, we specifically choose knowledge tracing to analyze the evolution of explanation methods in this context of temporal modeling and interpretations.