Addressing Two Problems in Deep Knowledge Tracing via
  Prediction-Consistent Regularization
  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.
