  We propose SAINT+, a successor of SAINT which is a Transformer based
knowledge tracing model that separately processes exercise information and
student response information. Following the architecture of SAINT, SAINT+ has
an encoder-decoder structure where the encoder applies self-attention layers to
a stream of exercise embeddings, and the decoder alternately applies
self-attention layers and encoder-decoder attention layers to streams of
response embeddings and encoder output. Moreover, SAINT+ incorporates two
temporal feature embeddings into the response embeddings: elapsed time, the
time taken for a student to answer, and lag time, the time interval between
adjacent learning activities. We empirically evaluate the effectiveness of
SAINT+ on EdNet, the largest publicly available benchmark dataset in the
education domain. Experimental results show that SAINT+ achieves
state-of-the-art performance in knowledge tracing with an improvement of 1.25%
in area under receiver operating characteristic curve compared to SAINT, the
current state-of-the-art model in EdNet dataset.
