Knowledge tracing plays a pivotal role in intelligent tutoring systems. This
task aims to predict the probability of students answering correctly to
specific questions. To do so, knowledge tracing systems should trace the
knowledge state of the students by utilizing their problem-solving history and
knowledge about the problems. Recent advances in knowledge tracing models have
enabled better exploitation of problem solving history. However, knowledge
about problems has not been studied, as well compared to students' answering
histories. Knowledge tracing algorithms that incorporate knowledge directly are
important to settings with limited data or cold starts. Therefore, we consider
the problem of utilizing skill-to-skill relation to knowledge tracing. In this
work, we introduce expert labeled skill-to-skill relationships. Moreover, we
also provide novel methods to construct a knowledge-tracing model to leverage
human experts' insight regarding relationships between skills. The results of
an extensive experimental analysis show that our method outperformed a baseline
Transformer model. Furthermore, we found that the extent of our model's
superiority was greater in situations with limited data, which allows a smooth
cold start of our model.

本文通过专家指导下的技能关系构建知识追踪模型，在限制数据情况下进行了广泛实验，并发现该模型在冷启动情况下性能表现更佳。