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. 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根据一套预先界定的技能来估计他们的掌握水平,根据这些技能可以准确预测相应的未来业绩。在实践中,学生的学习历史包含对一系列大规模问题的答案,每个称为会话,而不仅仅是一个独立答案的顺序。理论上,学生的学习动态在课程之内和之间会有很大不同。因此,如何有效地模拟学生在课程内部和之间知识状态的动态对于处理KT问题至关重要。大多数现有的KT模型将学生的学习记录作为单一连续顺序处理,而不记录学生知识状况的会间变化。为了解决上述问题,我们提议了一个全新的等级变异模型,称为HITSKT, 包括一个互动(级别)编码器,以捕捉学生在课程中获得的知识,以及一个(级别)会议(级别)的编码器,以总结以往会议获得的知识。预测本届会议的互动情况,一个州级知识检索者将总结的过去会议模式与以往互动模式的学生学习模式合并,同时记录学生们的知识状态,这些演示了当前互动过程的六期( ) 将显示学生在课程中的学习模式到正确的学习过程。 这些演示是最新的学习,让学生们在最新的学习中进行新的学习。