As the rapid development of Intelligent Tutoring Systems (ITS) in the past decade, tracing the students' knowledge state has become more and more important in order to provide individualized learning guidance. This is the main idea of Knowledge Tracing (KT), which models students' mastery of knowledge concepts (KCs, skills needed to solve a question) based on their past interactions on platforms. Plenty of KT models have been proposed and have shown remarkable performance recently. However, the majority of these models use concepts to index questions, which means the predefined skill tags for each question are required in advance to indicate the KCs needed to answer that question correctly. This makes it pretty hard to apply on large-scale online education platforms where questions are often not well-organized by skill tags. In this paper, we propose Q-matrix-based Attentive Knowledge Tracing (QAKT), an end-to-end style model that is able to apply the attentive method to scenes where no predefined skill tags are available without sacrificing its performance. With a novel hybrid embedding method based on the q-matrix and Rasch model, QAKT is capable of modeling problems hierarchically and learning the q-matrix efficiently based on students' sequences. Meanwhile, the architecture of QAKT ensures that it is friendly to questions associated with multiple skills and has outstanding interpretability. After conducting experiments on a variety of open datasets, we empirically validated that our model shows similar or even better performance than state-of-the-art KT methods. Results of further experiments suggest that the q-matrix learned by QAKT is highly model-agnostic and more information-sufficient than the one labeled by human experts, which could help with the data mining tasks in existing ITSs.

翻译：随着过去十年智能辅导系统（ITS）的快速发展，追踪学生的知识状态以提供个性化学习指导变得越来越重要。这是知识追踪（KT）的核心思想，它基于学生在平台上的历史交互来建模学生对知识概念（KCs，即解答问题所需的技能）的掌握程度。近年来，大量KT模型被提出并展现出卓越性能。然而，这些模型大多使用概念来索引问题，这意味着需要预先为每个问题定义技能标签，以指示正确回答该问题所需的KCs。这使得这些模型难以应用于大规模在线教育平台，因为这类平台上的问题往往缺乏规范的技能标签组织。本文提出了一种基于Q矩阵的注意力知识追踪模型（QAKT），这是一种端到端风格的模型，能够在无预定义技能标签的场景中应用注意力机制，且不牺牲性能。通过融合Q矩阵和Rasch模型的新型混合嵌入方法，QAKT能够分层建模问题，并基于学生答题序列高效学习Q矩阵。同时，QAKT的架构使其对多技能关联问题具有良好适应性，并展现出卓越的可解释性。在多个公开数据集上的实验表明，我们的模型表现出与最先进的KT方法相当甚至更优的性能。进一步实验结果表明，QAKT学习到的Q矩阵具有高度模型无关性，且比人类专家标注的Q矩阵包含更充分的信息，这有助于现有ITS中的数据分析任务。