通过预训练问题嵌入改进知识追踪
Improving Knowledge Tracing via Pre-training Question Embeddings
摘要：
知识追踪（KT）定义了一个任务，即根据学生的历史反应来预测他们是否能正确回答问题。虽然大量的研究致力于挖掘问题信息，丰富的高级信息的问题和技能还没有得到很好的提取，这使得以前的工作具有挑战性，充分执行。在本文中，我们证明了通过在丰富的边信息上为每个问题预训练嵌入，然后在获得的嵌入上训练深度KT模型，可以实现KT的大幅收益。具体而言，边信息包括问题难度和问题与技能之间的二分图中包含的三种关系。为了预训练问题嵌入，我们建议使用基于乘积的神经网络来恢复边信息。因此，在现有的深度KT模型中采用预训练的嵌入，在三个常见的KT数据集上的表现明显优于最先进的基线。
英文摘要：
Knowledge tracing (KT) defines the task of predicting whether students can correctly answer questions based on their historical response. Although much research has been devoted to exploiting the question information, plentiful advanced information among questions and skills hasn't been well extracted, making it challenging for previous work to perform adequately. In this paper, we demonstrate that large gains on KT can be realized by pre-training embeddings for each question on abundant side information, followed by training deep KT models on the obtained embeddings. To be specific, the side information includes question difficulty and three kinds of relations contained in a bipartite graph between questions and skills. To pre-train the question embeddings, we propose to use product-based neural networks to recover the side information. As a result, adopting the pre-trained embeddings in existing deep KT models significantly outperforms state-of-the-art baselines on three common KT datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
