  In the field of intelligent education, knowledge tracing (KT) has attracted
increasing attention, which estimates and traces students' mastery of knowledge
concepts to provide high-quality education. In KT, there are natural graph
structures among questions and knowledge concepts so some studies explored the
application of graph neural networks (GNNs) to improve the performance of the
KT models which have not used graph structure. However, most of them ignored
both the questions' difficulties and students' attempts at questions. Actually,
questions with the same knowledge concepts have different difficulties, and
students' different attempts also represent different knowledge mastery. In
this paper, we propose a difficulty and attempts boosted graph-based KT
(DAGKT), using rich information from students' records. Moreover, a novel
method is designed to establish the question similarity relationship inspired
by the F1 score. Extensive experiments on three real-world datasets demonstrate
the effectiveness of the proposed DAGKT.
