Transition-Aware Multi-Activity Knowledge Tracing
Transition-Aware Multi-Activity Knowledge Tracing
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DOI：
10.1109/bigdata55660.2022.10020617
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发表时间：
2022-12
期刊：
影响因子：
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通讯作者：
Siqian Zhao;Chunpai Wang;Shaghayegh Sherry Sahebi
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文献类型：
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作者：
Siqian Zhao;Chunpai Wang;Shaghayegh Sherry Sahebi
对于越来越多地用于学生培训的大规模在线学习系统，对学生知识的准确建模是必不可少的。知识追踪的目的是在给定学生的学习活动顺序的情况下对学生的知识状态进行建模。现代知识追踪(KT)通常被描述为一个有监督的序列学习问题，通过将学生的知识状态概括为一组不断变化的隐变量，根据学生过去观察到的练习成绩来预测学生未来的练习成绩。由于这种表述，当前的许多KT解决方案不适合于对学生从没有明确反馈或分数观察的非评估学习活动(例如，观看未评分的视频讲座)中的学习进行建模。此外，这些模型不能明确地表示不同学习活动之间的知识转移的动态，特别是在被评估的(例如，测验)和非评估的(例如，视频讲座)学习活动之间。在本文中，我们提出了转换感知多活动知识跟踪(TAMKOT)，它模拟了当学生在评估和未评估的学习材料之间或之内转换时，学习材料之间的知识转移，以及学生的知识。TAMKOT被描述为一种深度递归多活动学习模型，它通过激活和学习一组知识转移矩阵来显式地学习知识转移，每个矩阵对应于学生活动之间的每一种转移类型。因此，我们的模型允许在不同但可转移的潜在空间中表示每种材料类型，同时在共享空间中保持学生的知识。我们在三个公开可用的真实数据集上对我们的模型进行了评估，并展示了TAMKOT在预测学生表现和模拟知识转移方面的能力。
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 f or 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.