  Knowledge tracing, the act of modeling a student's knowledge through learning
activities, is an extensively studied problem in the field of computer-aided
education. Although models with attention mechanism have outperformed
traditional approaches such as Bayesian knowledge tracing and collaborative
filtering, they share two limitations. Firstly, the models rely on shallow
attention layers and fail to capture complex relations among exercises and
responses over time. Secondly, different combinations of queries, keys and
values for the self-attention layer for knowledge tracing were not extensively
explored. Usual practice of using exercises and interactions (exercise-response
pairs) as queries and keys/values respectively lacks empirical support. In this
paper, we propose a novel Transformer based model for knowledge tracing, SAINT:
Separated Self-AttentIve Neural Knowledge Tracing. SAINT has an encoder-decoder
structure where exercise and response embedding sequence separately enter the
encoder and the decoder respectively, which allows to stack attention layers
multiple times. To the best of our knowledge, this is the first work to suggest
an encoder-decoder model for knowledge tracing that applies deep self-attentive
layers to exercises and responses separately. The empirical evaluations on a
large-scale knowledge tracing dataset show that SAINT achieves the
state-of-the-art performance in knowledge tracing with the improvement of AUC
by 1.8% compared to the current state-of-the-art models.
