  Knowledge Tracing (KT), tracking a human's knowledge acquisition, is a
central component in online learning and AI in Education. In this paper, we
present a simple, yet effective strategy to improve the generalization ability
of KT models: we propose three types of novel data augmentation, coined
replacement, insertion, and deletion, along with corresponding regularization
losses that impose certain consistency or monotonicity biases on the model's
predictions for the original and augmented sequence. Extensive experiments on
various KT benchmarks show that our regularization scheme consistently improves
the model performances, under 3 widely-used neural networks and 4 public
benchmarks, e.g., it yields 6.3% improvement in AUC under the DKT model and the
ASSISTmentsChall dataset.
