Abstract
Accurate modeling of student knowledge is essential for large-scale online learning systems that are increasingly used for student training. Knowledge tracing aims to model student knowledge state given the student's sequence of learning activities. Modern Knowledge tracing (KT) is usually formulated as a supervised sequence learning problem to predict students' future practice performance according to their past observed practice scores by summarizing student knowledge state as a set of evolving hidden variables. Because of this formulation, many current KT solutions are not fit for modeling student learning from non-assessed learning activities with no explicit feedback or score observation (e.g., watching video lectures that are not graded). Additionally, these models cannot explicitly represent the dynamics of knowledge transfer among different learning activities, particularly between the assessed (e.g., quizzes) and non-assessed (e.g., video lectures) learning activities. In this paper, we propose Transition-Aware Multi-activity Knowledge Tracing (TAMKOT), which models knowledge transfer between learning materials, in addition to student knowledge, when students transition between and within assessed and non-assessed learning materials. TAMKOT is formulated as a deep recurrent multi-activity learning model that explicitly learns knowledge transfer by activating and learning a set of knowledge transfer matrices, one for each transition type between student activities. Accordingly, our model allows for representing each material type in a different yet transferrable latent space while maintaining student knowledge in a shared space. We evaluate our model on three real-world publicly available datasets and demonstrate TAMKOT's capability in predicting student performance and modeling knowledge transfer.
Abstract (translated)
准确建模学生知识对于日益用于学生培训的大型在线学习系统来说是至关重要的。知识追踪旨在根据学生的历史学习活动序列建模学生知识状态。现代知识追踪(KT)通常被表述为一种监督序列学习问题,以预测学生根据过去观察到的练习分数按其过去学习状态总结的学生未来实践表现。由于这种表述,许多当前的KT解决方案并不适合建模没有明确反馈或分数观察的非评估学习活动(例如,观看没有评分的视频讲座)。此外,这些模型无法明确代表不同学习活动之间的知识传递动态,特别是评估(例如,Quizzes)和非评估(例如,视频讲座)学习活动之间的知识传递动态。在本文中,我们提出了过渡 aware 的多活动知识追踪(TAMKOT),它在学生跨越和 within 评估和非评估学习材料之间的过渡时建模知识传递,而不仅仅是学生知识。TAMKOT被表述为一种深循环多活动学习模型,通过激活并学习一组知识传递矩阵,以 explicitly 学习知识传递,每个矩阵对应于学生活动之间的每种过渡类型。因此,我们的模型允许每种材料类型在一种不同的可转移的潜在空间中表示,同时保持学生知识在一个共享的空间中。我们评估了三个真实的公共可用数据集,并展示了 TAMKOT 在预测学生表现和建模知识传递方面的能力。
URL
https://arxiv.org/abs/2301.12916
