  Knowledge Tracing aims to assess student learning states by predicting their
performance in answering questions. Different from the existing research which
utilizes fixed-length learning sequence to obtain the student states and
regards KT as a static problem, this work is motivated by three dynamical
characteristics: 1) The scales of students answering records are constantly
growing; 2) The semantics of time intervals between the records vary; 3) The
relationships between students, questions and concepts are evolving. The three
dynamical characteristics above contain the great potential to revolutionize
the existing knowledge tracing methods. Along this line, we propose a Dynamic
Graph-based Knowledge Tracing model, namely DyGKT. In particular, a
continuous-time dynamic question-answering graph for knowledge tracing is
constructed to deal with the infinitely growing answering behaviors, and it is
worth mentioning that it is the first time dynamic graph learning technology is
used in this field. Then, a dual time encoder is proposed to capture long-term
and short-term semantics among the different time intervals. Finally, a
multiset indicator is utilized to model the evolving relationships between
students, questions, and concepts via the graph structural feature. Numerous
experiments are conducted on five real-world datasets, and the results
demonstrate the superiority of our model. All the used resources are publicly
available at https://github.com/PengLinzhi/DyGKT.
