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.

翻译：知识追踪在智能 tutoring 系统中扮演着关键角色。该任务旨在预测学生正确回答特定问题的概率。为此，知识追踪系统需利用学生的问题解决历史及问题相关知识来追踪学生的知识状态。近年来知识追踪模型的发展已能更好地利用问题解决历史，但相较于学生作答历史，问题相关知识的研究尚不充分。直接整合知识的知识追踪算法在数据有限或冷启动场景中至关重要。因此，我们考虑将技能间关系应用于知识追踪问题。本研究引入了专家标注的技能间关系，并提出了构建知识追踪模型的新方法，以利用人类专家对技能间关系的洞察。广泛实验分析结果表明，我们的方法优于基准Transformer模型。此外，我们发现模型优势在数据有限的情况下更为显著，这保障了模型的平稳冷启动。