Improving Knowledge Tracing through Embedding based on Metapath
Improving Knowledge Tracing through Embedding based on Metapath
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发表时间：
2021
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通讯作者：
Chong Jiang;Wenbin Gan;Guiping Su;Yuan Sun;Yi Sun
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作者：
Chong Jiang;Wenbin Gan;Guiping Su;Yuan Sun;Yi Sun
知识追踪（KT）的目标是跟踪学生的知识状况，并根据他们的学习日志预测他们未来的表现。虽然许多研究致力于利用输入信息，他们没有严格区分问题和所涉及的技能时，学习日志作为输入，从而导致性能下降，由于事实上，技能和问题之间的内在联系没有得到充分利用。为了解决这个问题，我们提出了一种基于元路径的嵌入式预训练方法，通过显式地考虑技能和领域中的问题之间的关系。具体而言，我们构建了一个由技能和问题组成的异构图，并使用metapath2vec方法获得节点的有意义嵌入，从而在保持技能和问题自身特征的同时，将显式关系信息嵌入到技能和问题的密集表示中.通过将这些预训练的嵌入到现有模型中，在三个公开的真实世界数据集上的实验表明，我们的方法实现了新的最先进的性能，具有至少1%的绝对AUC改进。
: The goal of knowledge tracing (KT) is to track students’ knowledge status and predict their future performance based on their learning logs. Although many researches have been devoted to exploiting the input information, they do not strictly distinguish between questions and the involved skills when taking the learning logs as input, and hence leading to performance degradation due to the fact that the inherent relations between skills and questions are not fully utilized. To solve this issue, we propose an embedding pre-training method based on metapath by explicitly considering the relations between skills and questions in the domain. Specifically, we construct a heterogeneous graph composed of skills and questions, and obtain the meaningful embeddings of nodes using the metapath2vec method, hence the explicit relation information can be embedded in the dense representation of skills and questions while still maintaining their own characteristics. Adopting these pre-trained embeddings to existing models, experiments on three public real-world datasets demonstrate that our method achieves the new state-of-the-art performance, with at least 1% absolute AUC improvement.