Addressing two problems in deep knowledge tracing via prediction-consistent regularization

Chun-Kit Yeung, D. Yeung
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引用次数: 146

Abstract

Knowledge tracing is one of the key research areas for empowering personalized education. It is a task to model students' mastery level of a knowledge component (KC) based on their historical learning trajectories. In recent years, a recurrent neural network model called deep knowledge tracing (DKT) has been proposed to handle the knowledge tracing task and literature has shown that DKT generally outperforms traditional methods. However, through our extensive experimentation, we have noticed two major problems in the DKT model. The first problem is that the model fails to reconstruct the observed input. As a result, even when a student performs well on a KC, the prediction of that KC's mastery level decreases instead, and vice versa. Second, the predicted performance for KCs across time-steps is not consistent. This is undesirable and unreasonable because student's performance is expected to transit gradually over time. To address these problems, we introduce regularization terms that correspond to reconstruction and waviness to the loss function of the original DKT model to enhance the consistency in prediction. Experiments show that the regularized loss function effectively alleviates the two problems without degrading the original task of DKT.1
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通过预测一致正则化解决深度知识跟踪中的两个问题
知识溯源是实现个性化教育的关键研究领域之一。这是一项基于学生历史学习轨迹的知识组件(KC)掌握水平建模的任务。近年来,人们提出了一种递归神经网络模型——深度知识跟踪(deep knowledge tracing, DKT)来处理知识跟踪任务,文献表明,DKT总体上优于传统方法。然而,通过我们广泛的实验,我们注意到了DKT模型中的两个主要问题。第一个问题是模型无法重建观测到的输入。因此,即使学生在KC上表现良好,对KC掌握水平的预测反而会下降,反之亦然。其次,跨时间步长的KCs的预测性能不一致。这是不可取的和不合理的,因为学生的表现是随着时间的推移而逐渐变化的。为了解决这些问题,我们在原始DKT模型的损失函数中引入了与重构和波浪度相对应的正则化项,以提高预测的一致性。实验表明,正则化损失函数在不影响DKT.1原任务的前提下,有效地缓解了这两个问题
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