  Knowledge tracing (KT) is the problem of predicting students' future
performance based on their historical interaction sequences. With the advanced
capability of capturing contextual long-term dependency, attention mechanism
becomes one of the essential components in many deep learning based KT (DLKT)
models. In spite of the impressive performance achieved by these attentional
DLKT models, many of them are often vulnerable to run the risk of overfitting,
especially on small-scale educational datasets. Therefore, in this paper, we
propose \textsc{sparseKT}, a simple yet effective framework to improve the
robustness and generalization of the attention based DLKT approaches.
Specifically, we incorporate a k-selection module to only pick items with the
highest attention scores. We propose two sparsification heuristics : (1)
soft-thresholding sparse attention and (2) top-$K$ sparse attention. We show
that our \textsc{sparseKT} is able to help attentional KT models get rid of
irrelevant student interactions and have comparable predictive performance when
compared to 11 state-of-the-art KT models on three publicly available
real-world educational datasets. To encourage reproducible research, we make
our data and code publicly available at
\url{https://github.com/pykt-team/pykt-toolkit}\footnote{We merged our model to
the \textsc{pyKT} benchmark at \url{https://pykt.org/}.}.
