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Learning from Interpretable Analysis: Attention-Based Knowledge Tracing

机译：从可解释分析中学习：基于注意力的知识跟踪

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Knowledge tracing is a well-established problem and non-trivial task in personalized education. In recent years, many existing works have been proposed to handle the knowledge tracing task, particularly recurrent neural networks based methods, e.g., Deep Knowledge Tracing (DKT). However, DKT has the problem of vibration in prediction outputs. In this paper, to better understand the problem of DKT, we utilize a mathematical computation model named Finite State Automat-on(FSA), which can change from one state to another in response to the external input, to interpret the hidden state transition of the DKT when receiving inputs. And we discover the root cause of the two problems is that the DKT can not handle the long sequence input with the help of FSA. Accordingly, we propose an effective attention-based model, which can solve the above problem by directly capturing the relationships among each item of the input regardless of the length of the input sequence. The experimental results show that our proposed model can significantly outperform state-of-the-art approaches on several well-known corpora.
机译：知识跟踪是个性化教育中一个既定的问题，也是一项艰巨的任务。近年来，已经提出了许多现有的工作来处理知识跟踪任务，特别是基于递归神经网络的方法，例如，深度知识跟踪（DKT）。但是，DKT在预测输出中存在振动问题。在本文中，为了更好地理解DKT问题，我们使用了一个名为有限状态自动机（FSA）的数学计算模型，该模型可以响应外部输入从一种状态变为另一种状态，以解释DKT的隐藏状态转换。接收输入时的DKT。我们发现这两个问题的根本原因是DKT无法在FSA的帮助下处理长序列输入。因此，我们提出了一种有效的基于注意力的模型，该模型可以通过直接捕获输入的每个项目之间的关系而与输入序列的长度无关地解决上述问题。实验结果表明，我们提出的模型可以显着优于几种知名语料库上的最新方法。

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