Knowledge tracing (KT) aims to leverage students' learning histories to estimate their mastery levels on a set of pre-defined skills, based on which the corresponding future performance can be accurately predicted. As an important way of providing personalized experience for online education, KT has gained increased attention in recent years. In practice, a student's learning history comprises answers to sets of massed questions, each known as a session, rather than merely being a sequence of independent answers. Theoretically, within and across these sessions, students' learning dynamics can be very different. Therefore, how to effectively model the dynamics of students' knowledge states within and across the sessions is crucial for handling the KT problem. Most existing KT models treat student's learning records as a single continuing sequence, without capturing the sessional shift of students' knowledge state. To address the above issue, we propose a novel hierarchical transformer model, named HiTSKT, comprises an interaction(-level) encoder to capture the knowledge a student acquires within a session, and a session(-level) encoder to summarise acquired knowledge across the past sessions. To predict an interaction in the current session, a knowledge retriever integrates the summarised past-session knowledge with the previous interactions' information into proper knowledge representations. These representations are then used to compute the student's current knowledge state. Additionally, to model the student's long-term forgetting behaviour across the sessions, a power-law-decay attention mechanism is designed and deployed in the session encoder, allowing it to emphasize more on the recent sessions. Extensive experiments on three public datasets demonstrate that HiTSKT achieves new state-of-the-art performance on all the datasets compared with six state-of-the-art KT models.

翻译：知识追踪（KT）旨在利用学生的学习历史来评估其对一组预定义技能的掌握程度，从而能够准确预测其未来的表现。作为为在线教育提供个性化体验的重要途径，KT近年来备受关注。在实践中，学生的学习历史由一系列成组密集问题的答案构成（每组成称为一个会话），而并非仅仅是独立答案的序列。理论上，在这些会话内部及跨会话之间，学生的学习动态可能存在显著差异。因此，如何有效建模学生在会话内和跨会话间的知识状态动态变化，是处理KT问题的关键。现有大多数KT模型将学生的学习记录视为单一连续序列，未能捕捉学生知识状态的会话性变化。针对上述问题，我们提出了一种新颖的分层Transformer模型——HiTSKT，该模型包含一个交互（层面）编码器，用于捕捉学生在一个会话内所获得的知识，以及一个会话（层面）编码器，用于总结跨过去各会话所获得的知识。为预测当前会话中的交互，一个知识检索器将总结出的过去会话知识与先前交互的信息整合为适当的知识表征，这些表征随后被用于计算学生当前的知识状态。此外，为模拟学生在跨会话间的长期遗忘行为，我们在会话编码器中设计并部署了一种幂律衰减注意力机制，使其更侧重于近期的会话。在三个公开数据集上进行的广泛实验表明，与六种最先进的KT模型相比，HiTSKT在所有数据集上均取得了新的最佳性能。