@article{Xu2026, 
author = {Jia Xu and Rongrong Tang and Pin Lv and Minghe Yu and Ge Yu and Enhong Chen},
title = {LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers},
year = {2026},
journal = {Tsinghua Science and Technology},
volume = {31},
number = {3},
pages = {1894-1917},
keywords = {Knowledge Tracing (KT), positive learning transfer, negative learning transfer, learning transfer graph, deep neural networks},
url = {https://www.sciopen.com/article/10.26599/TST.2024.9010201},
doi = {10.26599/TST.2024.9010201},
abstract = {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.}
}